From 68693e1f78dd66ca7fe53891dc5ee220e74f0948 Mon Sep 17 00:00:00 2001
From: 202310715084 PUTRI ADELIA AZIZAH <202310715084@mhs.ubharajaya.ac.id>
Date: Mon, 13 Jul 2026 03:59:32 +0700
Subject: [PATCH] Upload files to "/"

---
 ...NGANAN_UKT_DECISION_TREE_NAIVE_BAYES.ipynb | 3552 +++++++++++++++++
 README_STREAMLIT.md                           |   21 +
 2 files changed, 3573 insertions(+)
 create mode 100644 KLASIFIKASI_KELAYAKAN_KERINGANAN_UKT_DECISION_TREE_NAIVE_BAYES.ipynb
 create mode 100644 README_STREAMLIT.md

diff --git a/KLASIFIKASI_KELAYAKAN_KERINGANAN_UKT_DECISION_TREE_NAIVE_BAYES.ipynb b/KLASIFIKASI_KELAYAKAN_KERINGANAN_UKT_DECISION_TREE_NAIVE_BAYES.ipynb
new file mode 100644
index 0000000..5124727
--- /dev/null
+++ b/KLASIFIKASI_KELAYAKAN_KERINGANAN_UKT_DECISION_TREE_NAIVE_BAYES.ipynb
@@ -0,0 +1,3552 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZdkZTtAglYk6"
+      },
+      "source": [
+        "# KLASIFIKASI KELAYAKAN KERINGANAN UANG KULIAH TUNGGAL (UKT) MAHASISWA MENGGUNAKAN ALGORITMA DECISION TREE DAN NAIVE BAYES\n",
+        "\n",
+        "Notebook ini disusun untuk proyek data mining klasifikasi kelayakan keringanan UKT mahasiswa. Dataset yang digunakan berasal dari file `klasifikasi_mhs.csv` dan alur kerja notebook contoh yang diberikan disusun ulang agar fokus pada dua algoritma: Decision Tree dan Naive Bayes.\n"
+      ],
+      "id": "ZdkZTtAglYk6"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XUn0woULlYk7"
+      },
+      "source": [
+        "## 1. Tujuan Analisis\n",
+        "\n",
+        "Tujuan proyek ini adalah membangun model klasifikasi untuk memprediksi apakah mahasiswa layak menerima keringanan UKT berdasarkan beberapa atribut sosial-ekonomi keluarga.\n",
+        "\n",
+        "Target klasifikasi:\n",
+        "\n",
+        "| Nilai | Makna |\n",
+        "|---|---|\n",
+        "| 0 | Layak menerima keringanan UKT |\n",
+        "| 1 | Tidak layak menerima keringanan UKT |\n"
+      ],
+      "id": "XUn0woULlYk7"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3Ku1gJs3lYk7"
+      },
+      "source": [
+        "## 2. Asumsi Interpretasi Kolom\n",
+        "\n",
+        "| Kolom | Asumsi yang dipakai | Dasar |\n",
+        "|---|---|---|\n",
+        "| `Kelayakan Keringanan UKT` | 0 = Layak, 1 = Tidak Layak | Pola kuat pada data: penghasilan tinggi, tanggungan sedikit, dan pekerjaan yang relatif mampu cenderung berlabel 1; penghasilan rendah, tanggungan banyak, dan pekerjaan kurang mampu cenderung berlabel 0. |\n",
+        "| `Tempat Tinggal` | 0 = Rumah Sendiri, 1 = Bukan Rumah Sendiri | Asumsi logis kontekstual untuk kasus ekonomi UKT. Korelasi kolom ini terhadap label tampak lebih lemah, sehingga interpretasi ini tidak dapat dipastikan hanya dari data. |\n"
+      ],
+      "id": "3Ku1gJs3lYk7"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2hyLN6sRlYk8"
+      },
+      "source": [
+        "## 3. Import Library\n"
+      ],
+      "id": "2hyLN6sRlYk8"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "lBNvXsFSlYk8"
+      },
+      "outputs": [],
+      "source": [
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "\n",
+        "from pathlib import Path\n",
+        "\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "from sklearn.model_selection import train_test_split, cross_val_score, StratifiedKFold\n",
+        "from sklearn.compose import ColumnTransformer\n",
+        "from sklearn.preprocessing import OneHotEncoder\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
+        "from sklearn.naive_bayes import GaussianNB\n",
+        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
+        "\n",
+        "sns.set_theme(style='whitegrid', palette='Set2')\n",
+        "pd.set_option('display.max_columns', None)\n"
+      ],
+      "id": "lBNvXsFSlYk8"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ebBctTLalYk9"
+      },
+      "source": [
+        "## 4. Membaca Dataset\n",
+        "\n",
+        "Notebook ini mencoba membaca file CSV dari folder yang sama dengan notebook. Jika dijalankan di tempat lain, sesuaikan nilai `DATA_PATH`.\n"
+      ],
+      "id": "ebBctTLalYk9"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 241
+        },
+        "id": "V4Bp_DzjlYk9",
+        "outputId": "c97e649a-b7f1-40ff-e718-337e6d248a50"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Path dataset: /content/klasifikasi_mhs.csv\n",
+            "Ukuran dataset: 100 baris x 6 kolom\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Tempat Tinggal Pekerjaan Orang Tua  Penghasilan Orang Tua  \\\n",
+              "0               0                 PNS               10000000   \n",
+              "1               0           TNI/POLRI                8000000   \n",
+              "2               1              Petani                4000000   \n",
+              "3               1             Nelayan                3000000   \n",
+              "4               0               Buruh                2000000   \n",
+              "\n",
+              "   Jumlah Tanggungan Orang Tua  Kendaraan Kelayakan Keringanan UKT  \n",
+              "0                            3          1                        0  \n",
+              "1                            2          2                        1  \n",
+              "2                            4          0                        0  \n",
+              "3                            5          1                        0  \n",
+              "4                            2          1                        1  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c5f73000-18ff-43ca-96a7-23b59540a789\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Tempat Tinggal</th>\n",
+              "      <th>Pekerjaan Orang Tua</th>\n",
+              "      <th>Penghasilan Orang Tua</th>\n",
+              "      <th>Jumlah Tanggungan Orang Tua</th>\n",
+              "      <th>Kendaraan</th>\n",
+              "      <th>Kelayakan Keringanan UKT</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>PNS</td>\n",
+              "      <td>10000000</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0</td>\n",
+              "      <td>TNI/POLRI</td>\n",
+              "      <td>8000000</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Petani</td>\n",
+              "      <td>4000000</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Nelayan</td>\n",
+              "      <td>3000000</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "      <td>Buruh</td>\n",
+              "      <td>2000000</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c5f73000-18ff-43ca-96a7-23b59540a789')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c5f73000-18ff-43ca-96a7-23b59540a789 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c5f73000-18ff-43ca-96a7-23b59540a789');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "raw_df",
+              "summary": "{\n  \"name\": \"raw_df\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"Tempat Tinggal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pekerjaan Orang Tua\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"TNI/POLRI\",\n          \"Ibu Rumah Tangga\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Penghasilan Orang Tua\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2533128,\n        \"min\": 700000,\n        \"max\": 10000000,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          7000000,\n          8000000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jumlah Tanggungan Orang Tua\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Kendaraan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Kelayakan Keringanan UKT\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"0\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ],
+      "source": [
+        "candidate_paths = [\n",
+        "    Path('klasifikasi_mhs.csv'),\n",
+        "    Path('klasifikasi_mhs (1).csv'),\n",
+        "    Path.cwd() / 'klasifikasi_mhs.csv',\n",
+        "    Path.cwd() / 'klasifikasi_mhs (1).csv',\n",
+        "]\n",
+        "\n",
+        "DATA_PATH = next((path for path in candidate_paths if path.exists()), None)\n",
+        "if DATA_PATH is None:\n",
+        "    raise FileNotFoundError('File klasifikasi_mhs.csv tidak ditemukan. Letakkan CSV di folder yang sama dengan notebook.')\n",
+        "\n",
+        "raw_df = pd.read_csv(DATA_PATH)\n",
+        "print(f'Path dataset: {DATA_PATH.resolve()}')\n",
+        "print(f'Ukuran dataset: {raw_df.shape[0]} baris x {raw_df.shape[1]} kolom')\n",
+        "raw_df.head()\n"
+      ],
+      "id": "V4Bp_DzjlYk9"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oEjRRV88lYk-"
+      },
+      "source": [
+        "## 5. Pemeriksaan Awal Data\n"
+      ],
+      "id": "oEjRRV88lYk-"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 481
+        },
+        "id": "AK9-ukxUlYk-",
+        "outputId": "7b3bc930-b960-48b9-f1eb-49370ecfd170"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 100 entries, 0 to 99\n",
+            "Data columns (total 6 columns):\n",
+            " #   Column                       Non-Null Count  Dtype \n",
+            "---  ------                       --------------  ----- \n",
+            " 0   Tempat Tinggal               100 non-null    int64 \n",
+            " 1   Pekerjaan Orang Tua          100 non-null    object\n",
+            " 2   Penghasilan Orang Tua        100 non-null    int64 \n",
+            " 3   Jumlah Tanggungan Orang Tua  100 non-null    int64 \n",
+            " 4   Kendaraan                    100 non-null    int64 \n",
+            " 5   Kelayakan Keringanan UKT     100 non-null    object\n",
+            "dtypes: int64(4), object(2)\n",
+            "memory usage: 4.8+ KB\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "                             jumlah_missing  persentase_missing  jumlah_unik\n",
+              "Tempat Tinggal                            0                 0.0            2\n",
+              "Pekerjaan Orang Tua                       0                 0.0            8\n",
+              "Penghasilan Orang Tua                     0                 0.0           10\n",
+              "Jumlah Tanggungan Orang Tua               0                 0.0            5\n",
+              "Kendaraan                                 0                 0.0            3\n",
+              "Kelayakan Keringanan UKT                  0                 0.0            3"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6f286bac-3391-4857-932a-81e8af34e598\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>jumlah_missing</th>\n",
+              "      <th>persentase_missing</th>\n",
+              "      <th>jumlah_unik</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Tempat Tinggal</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Pekerjaan Orang Tua</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Penghasilan Orang Tua</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Jumlah Tanggungan Orang Tua</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Kendaraan</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Kelayakan Keringanan UKT</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6f286bac-3391-4857-932a-81e8af34e598')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-6f286bac-3391-4857-932a-81e8af34e598 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-6f286bac-3391-4857-932a-81e8af34e598');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"print('Jumlah data duplikat:', raw_df\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"jumlah_missing\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"persentase_missing\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"jumlah_unik\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 2,\n        \"max\": 10,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Jumlah data duplikat: 77\n"
+          ]
+        }
+      ],
+      "source": [
+        "raw_df.info()\n",
+        "\n",
+        "display(pd.DataFrame({\n",
+        "    'jumlah_missing': raw_df.isna().sum(),\n",
+        "    'persentase_missing': (raw_df.isna().mean() * 100).round(2),\n",
+        "    'jumlah_unik': raw_df.nunique()\n",
+        "}))\n",
+        "\n",
+        "print('Jumlah data duplikat:', raw_df.duplicated().sum())\n"
+      ],
+      "id": "AK9-ukxUlYk-"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3pJIQniflYk-",
+        "outputId": "925dc304-dfba-4b75-ddfe-60c2fd4c0702"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "=== Tempat Tinggal ===\n",
+            "Tempat Tinggal\n",
+            "0    50\n",
+            "1    50\n",
+            "Name: count, dtype: int64\n",
+            "\n",
+            "=== Pekerjaan Orang Tua ===\n",
+            "Pekerjaan Orang Tua\n",
+            "Ibu Rumah Tangga    18\n",
+            "PNS                 13\n",
+            "Petani              13\n",
+            "TNI/POLRI           13\n",
+            "Wiraswasta          12\n",
+            "Buruh               12\n",
+            "Guru                12\n",
+            "Nelayan              7\n",
+            "Name: count, dtype: int64\n",
+            "\n",
+            "=== Penghasilan Orang Tua ===\n",
+            "Penghasilan Orang Tua\n",
+            "3000000     19\n",
+            "8000000     13\n",
+            "7000000     12\n",
+            "4000000     12\n",
+            "2000000     12\n",
+            "6000000     12\n",
+            "9000000      7\n",
+            "10000000     6\n",
+            "5000000      6\n",
+            "700000       1\n",
+            "Name: count, dtype: int64\n",
+            "\n",
+            "=== Jumlah Tanggungan Orang Tua ===\n",
+            "Jumlah Tanggungan Orang Tua\n",
+            "2    49\n",
+            "4    18\n",
+            "3    14\n",
+            "1    12\n",
+            "5     7\n",
+            "Name: count, dtype: int64\n",
+            "\n",
+            "=== Kendaraan ===\n",
+            "Kendaraan\n",
+            "1    57\n",
+            "2    25\n",
+            "0    18\n",
+            "Name: count, dtype: int64\n",
+            "\n",
+            "=== Kelayakan Keringanan UKT ===\n",
+            "Kelayakan Keringanan UKT\n",
+            "1      64\n",
+            "0      35\n",
+            "1 1     1\n",
+            "Name: count, dtype: int64\n"
+          ]
+        }
+      ],
+      "source": [
+        "for kolom in raw_df.columns:\n",
+        "    print(f'\\n=== {kolom} ===')\n",
+        "    print(raw_df[kolom].value_counts(dropna=False))"
+      ],
+      "id": "3pJIQniflYk-"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iMJSx_yElYk-"
+      },
+      "source": [
+        "## 6. Pembersihan Data\n",
+        "\n",
+        "Pada target ditemukan nilai `1 1`. Nilai ini diasumsikan sebagai kesalahan penulisan dan diperbaiki menjadi `1` karena format target seharusnya hanya 0 atau 1.\n"
+      ],
+      "id": "iMJSx_yElYk-"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 293
+        },
+        "id": "obQJTrzqlYk_",
+        "outputId": "2051184b-953b-4789-b4a6-8c7cd51936f7"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   Tempat Tinggal Pekerjaan Orang Tua  Penghasilan Orang Tua  \\\n",
+              "0               0                 PNS               10000000   \n",
+              "1               0           TNI/POLRI                8000000   \n",
+              "2               1              Petani                4000000   \n",
+              "3               1             Nelayan                3000000   \n",
+              "4               0               Buruh                2000000   \n",
+              "\n",
+              "   Jumlah Tanggungan Orang Tua  Kendaraan  Kelayakan Keringanan UKT  \\\n",
+              "0                            3          1                         0   \n",
+              "1                            2          2                         1   \n",
+              "2                            4          0                         0   \n",
+              "3                            5          1                         0   \n",
+              "4                            2          1                         1   \n",
+              "\n",
+              "  Label Kelayakan  \n",
+              "0           Layak  \n",
+              "1     Tidak Layak  \n",
+              "2           Layak  \n",
+              "3           Layak  \n",
+              "4     Tidak Layak  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-867d22b8-1c96-4eee-b4ac-3b765c8998ee\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Tempat Tinggal</th>\n",
+              "      <th>Pekerjaan Orang Tua</th>\n",
+              "      <th>Penghasilan Orang Tua</th>\n",
+              "      <th>Jumlah Tanggungan Orang Tua</th>\n",
+              "      <th>Kendaraan</th>\n",
+              "      <th>Kelayakan Keringanan UKT</th>\n",
+              "      <th>Label Kelayakan</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>PNS</td>\n",
+              "      <td>10000000</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0</td>\n",
+              "      <td>TNI/POLRI</td>\n",
+              "      <td>8000000</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Petani</td>\n",
+              "      <td>4000000</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Nelayan</td>\n",
+              "      <td>3000000</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "      <td>Buruh</td>\n",
+              "      <td>2000000</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-867d22b8-1c96-4eee-b4ac-3b765c8998ee')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-867d22b8-1c96-4eee-b4ac-3b765c8998ee button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-867d22b8-1c96-4eee-b4ac-3b765c8998ee');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"print(df['Kelayakan Keringanan UKT']\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Tempat Tinggal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pekerjaan Orang Tua\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"TNI/POLRI\",\n          \"Buruh\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Penghasilan Orang Tua\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3435112,\n        \"min\": 2000000,\n        \"max\": 10000000,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8000000,\n          2000000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jumlah Tanggungan Orang Tua\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Kendaraan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Kelayakan Keringanan UKT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label Kelayakan\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Tidak Layak\",\n          \"Layak\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Kelayakan Keringanan UKT\n",
+            "0    35\n",
+            "1    65\n",
+            "Name: count, dtype: int64\n"
+          ]
+        }
+      ],
+      "source": [
+        "df = raw_df.copy()\n",
+        "\n",
+        "# Membersihkan target agar hanya berisi 0 dan 1.\n",
+        "df['Kelayakan Keringanan UKT'] = (\n",
+        "    df['Kelayakan Keringanan UKT']\n",
+        "    .astype(str)\n",
+        "    .str.strip()\n",
+        "    .replace({'1 1': '1'})\n",
+        "    .astype(int)\n",
+        ")\n",
+        "\n",
+        "label_map = {0: 'Layak', 1: 'Tidak Layak'}\n",
+        "df['Label Kelayakan'] = df['Kelayakan Keringanan UKT'].map(label_map)\n",
+        "\n",
+        "display(df.head())\n",
+        "print(df['Kelayakan Keringanan UKT'].value_counts().sort_index())\n"
+      ],
+      "id": "obQJTrzqlYk_"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZhRRUR38lYk_"
+      },
+      "source": [
+        "## 7. Analisis Eksploratif Data\n"
+      ],
+      "id": "ZhRRUR38lYk_"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 367
+        },
+        "id": "gGziaQlXlYk_",
+        "outputId": "ad4bf191-b59e-4c6f-cbbf-d5ff027de013"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ],
+            "image/png": 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3r7jiCtPhcLiPW716tdm6dWuPtuDZJCUlma1btzbnzp3r3lZcm6o8bZ6iFNRz+eWXm7m5ue7tixcvNlu3bm1++OGHpmmaZkZGhtm1a1fzH//4h8f5J06cMM8//3yP7XfffbfZunVr88knn/Q4tuDeP/jggx7b77zzzkLXX9Q1/fDDD4XeE5dddpk5adKkM15jUWWtW7fObN26tfm///3Pva2gfXrnnXcWOt6f2neHDh0yW7dubb700ktnPK6o67755pvNgQMHemzr37+/2bp1a3PTpk3ubenp6Wbv3r09/kZ7/vnnzU6dOpn79+/3OP/JJ58027RpYx45csTj+k6/n+JdGnYnUkKhoaHurqpFKfhW8aOPPipzl86goCBGjx5d4uNHjRpFWFiY+/Gll15KnTp12Lx5c6nrjoiI4OjRo2zfvr3U55ZFUFCQe14Dp9PJqVOnCA0NJTY2tsjhZWe71p07d5KQkMCIESM4deoUycnJJCcnk5WVRa9evfjf//5X5vty8uRJQkNDqVOnTqF9KSkpbNmyhSFDhpCRkeGu99SpU/Tp04eEhAT3kLiinN5TJysri+TkZDp37oxpmh7Pw8iRIzl+/LjHt85r164lJCSESy65pNTlFdiyZQsTJkygV69ezJs3r9D8YqeXl5qaSnp6Oueff36RZV1wwQUeE5Oed955hIWFcejQoWKvvzxSU1PJy8ujXr166vEkItXajTfeSK9evejXrx933HEHNWrUYP78+cTExPDBBx/gcrkYMmSI+zMoOTmZ2rVr07Rp00K9lUJDQz3m6wkKCiI+Pt7jd/Xnn39OTEwMAwcOdG8LDg4utvd3ScoMCAhwf8a4XC5SUlLIy8ujffv2Hp8pERER2O12j+FIZVUZn2GZmZnUqFHjjMcU7P/r6smjRo0q9PlV2vbR6NGjPT67u3btCuCOe8eOHaSkpHDVVVd5zFU6YsQIIiMjzxh3RSttG6UoV199tcdE89deey2BgYHu9uBXX31FWloaw4YN83g/WCwWOnbsWGTvvWuvvdbjcUFZfx2SWFTPu9OvyeFwcOrUKZo0aUJERESh1/WePXtISEgo9tpOLysnJ4fk5GT3UM6iVle+5pprii2rKrXvTo8lPT2d5ORkunfvzqFDh0hPT/c4tm7duh4jCsLCwhg1ahS//PILJ06cAPJ72J1//vlERER4vAYuuOACnE4n//vf/yokbik9DbsTKaGsrKwzDoMbOnQob7/9Nv/4xz946qmn6NWrF4MGDeLSSy8t8eSRMTExpZpc/PSu2wCGYdC0aVMSExNLXEaBiRMn8tVXXzFmzBiaNm1K7969GT58uHt4YEVzuVy8+uqrvPHGGxw+fBin0+neVzCM4HRnu9aCD/MzrSaTnp5epobWv//9b/7v//6Pm2++mTfeeMPjdXDw4EFM0+S5557jueeeK/L8pKSkQt3kCxw5coS5c+fy8ccfF5qz4PRGau/evalTpw5r1qyhV69euFwu1q1bx8CBAz2SciUtD/IbNrfccgvt2rXj2WefLXIC/U8++YQFCxawc+dOcnNz3dv/2vUboH79+oW2RUZGkpaWVuS1l1evXr2oX78+b775JpGRkfzjH//wSj0iIr52//33ExsbS0BAALVr1yY2NtbdtkhISMA0TY8vIk7319/t9erVK/Q7PDIykl27drkfJyYm0qRJk0LHnf4HaGnLBHjnnXdYunQp+/fvx+FwuLefPgzquuuu47333mPixInExMTQu3dvhgwZwoUXXlhk3WdSGZ9hNWrUKDQnz18VfHn51yRVUQtglLZ91KBBA4/HBV+GFsR95MgRoPC9CwwMpGHDhmeMu6KVpo1SnL+2B2vUqEGdOnUKtQeLG6J5epsJ8p+Hvw49TExMxGKxFHrOmjdvXqi87OxsFi5cyOrVqzl27JjH8LfTEyfTp09nypQpDB48mNatW9OnTx9GjhzpMaF7SkoK8+fPZ8OGDSQlJXnU89ckDBT9+oGq17777rvvmDdvHj/++CN2u91jX3p6OuHh4e7HTZs2LRRjs2bNgPz7VqdOHQ4cOMCuXbvo1atXkfUlJydXSNxSeko+iZTA0aNHSU9PL7bRBflZ+9dff52tW7fy6aef8vnnn7Nhwwbeeustli5dWqJV4iqz94bT6fSIqUWLFmzcuNEd+/vvv88bb7zBbbfdxvTp0yu8/hdffJHnnnuOK664ghkzZhAZGYnFYuGxxx4rdtz6mRSc8/e//909b8RflXUVkG7duvHss88ybdo0xo8fz/Lly90fhAW9qW6++Wb69u1b5PnFvW6cTic33XSTe66M5s2bExoayrFjx5g1a5ZHT62AgABGjBjBihUrePDBB/n+++85fvw4l112WZnKg/xvVy+88EI+/vhjPv/8c/r37++x/9tvv+XWW2+lW7duPPDAA9SpUwer1cqqVatYt25doesp7jV+tvtZMEF4cROO2u32YicRv//++0lLS2P58uVERkYybdq0M9YlIlIVdejQodBCFwVcLheGYbB48eIifw//9bPPG6vWlqTM//73v8yaNYuLL76Y8ePHU6tWLQICAli4cKFHD4patWrx7rvv8sUXX/DZZ5/x2WefsXr1akaNGsWcOXNKHFNlfYa1aNGCnTt3cuTIkUKJoAIFSbiWLVt6bC+q3Vfa9lFxX3CWpS1VFkUlK4BCbY7StlHKquC6n3jiiSJ7rP/1Pp/e06wsHnnkEff8op06dSI8PBzDMArNw9StWzc++OADPvroI7788ktWrlzJsmXLeOihhxgzZgwAt99+Oz/88APjx4+nTZs2hIaG4nK5mDBhQpH3s7i2kb+070ri4MGD3HjjjTRv3pxZs2ZRv359rFYrmzdv5pVXXinT68LlctG7d28mTJhQ5P6CZJVUPiWfRErgv//9LwB9+vQ543EWi4VevXrRq1cvZs+ezYsvvsgzzzzD1q1bueCCC4r9gC6rv65mYpomBw4c8FiFr7hvJo4cOULjxo09toWGhjJ06FCGDh1Kbm4u06ZN48UXX+SWW26p8BXENm3aRI8ePXjsscc8tqelpRU5eeHZrrXgWsLCwrjgggsqNFbIXzXwn//8J7NmzeKWW25h6dKlhISEuOu1Wq2lrnf37t0kJCQwZ84cj4lKixtqMHLkSJYuXcrHH3/MZ599RnR0tMdrsrTlGYbBk08+yZQpU5gxYwaLFy+mR48e7v2bNm0iODiYJUuWePTIW7VqVamu82wKGuv79+8vcv/+/fuLbdBbLBbmzJlDeno68+fPJzIyknHjxlVofCIi/qxJkyaYpkmjRo2IjY2tkDIbNmzI3r17MU3To+1SMNF5WWzatInGjRszf/58jzKLmmg6KCiIAQMGMGDAAFwuFw8++CBvvfUWU6ZMKdTz5Uz1VcZn2EUXXcS6det49913mTJlSqH9GRkZfPTRRzRv3rxEsZe2fXQ2BZ+fBw8edK+SCPkTbScmJhZaubm0Cnpa/bVnzl974Ze2jVKcAwcOeFxHZmYmJ06ccPeMK2iX1apVq8ztwYYNG+JyuTh48KBHb6d9+/YVOnbTpk2MGjXKY2XpnJycInsqRUVFccUVV3DFFVeQmZnJ2LFjmTdvHmPGjCE1NZWvv/6aadOmMXXqVPc5ZxqmVxx/ad+VxMcff0xubi4LFizwaOsVt7jBgQMHCv1eKniOCnryNWnShKysLK/8PSDlozmfRM7i66+/5oUXXqBRo0YevUz+KiUlpdC2gh44Bd1ZC1a8qKhuqu+++65HN+WNGzd6fABD/ofwtm3bPLrUfvLJJ4WWX/5rl/GgoCBatGiBaZoeXeMrSkBAQKFvTN57771i50c627W2b9+eJk2asHTp0iLn5qqILrajRo3innvu4bvvvmPatGk4HA5q1apF9+7deeuttzh+/Hip6i34pu3058E0TV599dUijz/vvPOIi4tj5cqVvP/++wwbNsyjK3Vpy4P8+zx//nzi4+OZPHmyx5xfAQEBGIbh0eX/8OHDfPTRR8WWVxZ169alTZs2rF27ttB7Y8eOHWzbtu2Mwy2sVitz586lS5cuPPbYY7z77rsVGp+IiD+75JJLCAgIYP78+YU+V03TPOuQsKL06dOHY8eOefy+z8nJYcWKFWWOs6D3xOkxbtu2jR9//NHjuL/Ga7FY3AmS09syJamvMj7DBg8eTMuWLVm8eDE//fSTxz6Xy8UDDzxAamqqR0LhbHGXpn10Nu3btycqKooVK1aQl5fn3r527dpCQ9/KoqB39+nz6DidzkKvlbK0UYry1ltvebRL33zzTfLy8tzthL59+xIWFsbChQuLbL+WpD1YUNby5cs9ti9btqzQsUX1Clq+fLnH6w4Kv65r1KhBkyZN3K/p4noXFVVnSfhD+64kivq9kJ6eXmwi7Pjx43zwwQfuxxkZGbz77ru0adPG3dNtyJAh/PDDD3z++eeFzk9LS/N4H0jlUs8nkdN89tln7Nu3D6fTycmTJ9m6dStffvklDRo0YMGCBWfs/fP888/z7bff0q9fPxo2bEhSUhJvvPEG9erVc8+bVDAB4X/+8x9q1KhBaGgoHTp0KNQDqaQiIyO57rrrGD16NElJSSxbtoymTZt6TAg6ZswYNm3axIQJExgyZAgHDx5k7dq1hYaCjR8/ntq1a9OlSxdq1arFvn37eO211+jXr1+h8fEl9corrxTqUm6xWJg8eTIXXXQRzz//PLNnz6Zz587s3r2btWvXFvtcnO1aLRYLjz76KBMnTmT48OGMHj2amJgYjh07xtatWwkLC+PFF18s03Wcbty4caSmpjJ//nzuvvtunnzySR544AGuu+46RowYwVVXXUXjxo05efIkP/74I0ePHmXNmjVFltW8eXOaNGnCnDlzOHbsGGFhYWzatOmMycnThx38NRlalvIgv9v/woULGTduHBMnTmT58uXu5bFffvllJkyYwPDhw92v6SZNmhSax6O8Zs2axYQJExg1ahSXX345devW5bfffmPFihXUqVOHW2655Yzn22w2Fi1axNixY7n33nvdS1uLiFR3TZo04fbbb+epp54iMTGRiy++mBo1anD48GE+/PBDrrrqKsaPH1+qMq+++mpee+017rrrLsaNG0edOnVYu3atux1Ulp7cF110Ee+//z633XYbF110EYcPH+Y///kPLVu2JCsry33cP/7xD1JTU+nZsycxMTEcOXKE1157jTZt2tCiRYsS11dZn2FBQUHMnTuXG264wd1Oad++Penp6axbt46ff/6Zm2++mWHDhpWovNK2j0oS37Rp03jkkUe44YYbGDJkCImJiaxevfqM00mUVKtWrejUqRNPP/00qampREZGsmHDhkJ/4Je1jfJXDoeDG2+8kSFDhrB//37eeOMNzj//fPdnflhYGA8++CB///vfGT16NEOHDiU6OpojR46wefNmunTpwv3333/GOtq0acPw4cN54403SE9Pp3PnzmzZsqVQL3zIv1///e9/CQsLo2XLlvz444989dVXhebnGjZsGN27d6ddu3ZERUXx008/sWnTJsaOHeuOu1u3brz00ks4HA5iYmL48ssvOXz4cKmen9P5Q/sO8r/Iz8nJKbT94osvpnfv3litViZPnsw111xDZmYmb7/9NrVq1XJPIH66Zs2ace+99/LTTz9Rq1YtVq1aRVJSEo8//rj7mPHjx/Pxxx8zefJkLr/8ctq1a4fdbmf37t1s2rSJjz76iOjo6Aq/Tjk7JZ9ETlPQ9dtqtRIVFUXr1q255557GD169FkTMAMGDCAxMZFVq1Zx6tQpatasSffu3Zk2bZp7fiCr1cq//vUvnn76aR588EHy8vJ4/PHHy9ygmDx5Mrt27WLRokVkZmbSq1cvHnjgAXcPK8j/BmjWrFm8/PLLPPbYY7Rv354XX3yx0LwJV199NWvXruXll18mKyuLevXqcf311xfZhbykFi5cWGhbQEAAkydPZvLkydjtdtauXcuGDRto27YtCxcu5Kmnnirztfbo0YO33nqLF154gddee42srCzq1KlDhw4duPrqq8t8HX81bdo0UlNT3XM/PfTQQ6xatYr58+fzzjvvkJKSQnR0NG3btuW2224rthyr1cqLL77Io48+ysKFCwkODmbQoEH87W9/81g16HQjRozgySefpHHjxnTo0KHc5RUICwtjyZIljB07lptvvpnXX3+dXr168c9//pPFixfz2GOP0ahRI2bOnEliYmKFN0569uzJ66+/zoIFC1i+fDmZmZnUqlWL4cOHM23atDNO9l8gPDycJUuWcN1113HHHXcU6mYuIlJdTZo0iWbNmvHKK6/w/PPPA/mTgPfu3ZsBAwaUurwaNWqwbNkyHn30UV599VVCQ0MZNWoUnTt3Ztq0aWUaij969GhOnjzJW2+9xRdffEHLli3597//zcaNG/nmm2/cx1122WWsWLGCN954g7S0NOrUqcOQIUOYNm1aqebmqczPsBYtWrBmzRoWLVrExx9/zOrVqwkJCaF9+/YsWLCgVPegtO2jkhg7diymafLyyy8zZ84czjvvPBYsWMCjjz5aIdMqPPnkk9x///0sWrSIiIgIrrzySnr06MFNN93kPqY8bZTT3X///axdu5a5c+ficDgYNmwY//jHPzwSoiNGjKBu3bosWrSIJUuWkJubS0xMDF27di3xqtKPPfYYNWvWZO3atXz00Uf06NGDRYsW0a9fP4/j7r33XiwWC2vXriUnJ4cuXbq4Ezunu/766/n444/58ssvyc3NpUGDBtx+++0eieGnnnqKRx55hDfeeAPTNOnduzeLFy8udk7RkvB1+w7yV88sqhdSw4YNGTlyJHPnzuXZZ59lzpw51K5dm2uvvZbo6GjuueeeQuc0a9aM++67jyeeeIL9+/fTqFEjnnnmGY/nyGazsXz5chYuXMjGjRt59913CQsLo1mzZh5/l0nlM8zKmo1ORETKLTk5mb59+zJlypQzJrZEREQq2iuvvMLjjz/OZ599VuwqrlI1uFwu98rMjz76qK/DEZFzgOZ8EhGpQt555x2cTmepviUUEREprb+uQJqTk8Nbb71Fs2bNlHiqYnJycgrNI/Xuu++SkpJC9+7dfRSViJxrNOxORKQK+Prrr/ntt9948cUXufjii2nUqJGvQxIRkWps6tSpNGjQgPPOO4+MjAzWrFnDvn37ePLJJ30dmpTSjz/+yOOPP86ll15KVFQUv/zyCytXrqR169Zceumlvg5PRM4RSj6JiFQBL7zwAj/88AOdO3fmvvvu83U4IiJSzfXp04eVK1eydu1anE4nLVu25JlnnmHo0KG+Dk1KqWHDhtSrV4/ly5e7JwUfOXIkM2fOJCgoyNfhicg5QnM+iYiIiIiIiIiI12jOJxERERERERER8Roln0RERERERERExGs05xPwww8/YJomVqvV16GIiIiIlzkcDgzDoHPnzr4OpVpSu0pEROTcUdJ2lXo+AaZpFlp+VERERKonfe57l55f7zJNk9zcXD3HfkL3w7/ofvgP3Qv/4s37UdLPffV8Avc3c/Hx8T6ORERERLztp59+8nUI1ZraVd6VlZXFzp07admyJaGhob4O55yn++FfdD/8h+6Ff/Hm/Shpu0o9n0RERERERERExGuUfBIREREREREREa9R8klERERERERERLxGyScREREREREREfEaJZ9ERERERERERMRrtNqdiIiIiIiISDXldDpxOByVWmdOTo77p8WiPi++Vtb7YbVaCQgIqJAYlHwSERERERERqWZM0+To0aOkpKRUet0ul4vAwECOHDmi5JMfKM/9iIqKol69ehiGUa4YlHwSERERERERqWYKEk9169YlNDS03MmD0nA6neTk5BAcHFxhPWek7MpyP0zTJCsri+PHjwNQv379csWg5JOIiIiIiIhINeJ0Ot2Jp1q1avmkfoCQkBAln/xAWe+HzWYD4Pjx49StW7dc91L930RERERERESqkYI5nkJDQ30ciVR1Ba+h8s4bpuSTiIiIiIiISDVUmUPtpHqqqNeQkk8iIiIiIiIiIuI1Sj6JiHiZy3T5OgSRKkPvFxH/Y5qmr0Mok6oat0hVc/jwYeLi4liyZEmFlbl161bi4uLYunVrhZVZEgXXsnr16kqttyjz5s0jLi6O5ORkX4dSITThuIiIl1kMC69v+5xjGam+DkXEr8WERfK3jn19HYaI/IVhGHx/8iAZjhxfh1JiYdZgutRu4uswRPzW6tWrmT17NitXriQ+Pt7X4ZRZcdeRnp7OTTfdxK5du3j++ee58MILfRilgJJPIiKV4lhGKolp1eNbCxGpXAcOHGDJkiVs27aNPXv20Lx5c9atW+fef/jwYQYOHFjkuUFBQfz000/Flr1161bGjRtXaPvQoUN55plnyh+8VBsZjhxSc+2+DkNE5KwyMjK4+eab2bVrF/Pnz1fiyU8o+SQiIiLix/bs2cPmzZvp2LEjLper0FCiunXr8tZbb3lsM02TCRMm0LNnzxLV8fjjj9O8eXP345o1a5Y/cBERkUqWkZHB+PHj2blzJ/Pnz6dfv36+Dkn+oDmfRERERPzYgAED2Lx5M3PnzqVdu3aF9gcFBdGpUyePf7m5uWRkZDB8+PAS1dGqVSuP85s2bVrRlyEiIlVMbm4uzz33HKNHj+b888+nU6dOXHfddWzZsqXYc1555RX69+9P586dmTBhAnv27Cl0zG+//cb06dPp3r078fHxjB49mo8++qjc8WZmZjJhwgR+/vln5s2bx0UXXeSx/9ixY8yePZsLLriA9u3bM2zYMFauXHnWcn/99VdmzZrFwIEDiY+Pp3fv3syePZtTp065j9myZQtxcXF88MEHhc5fu3YtcXFx/PDDDyUurziJiYkMGjSI4cOHc/LkSQBWrVrFuHHj6NWrF+3bt2fo0KG88cYbhc4dNmwYt956K99++y1XXnkl8fHxDBw4kHffffes9VYE9XwSERER8WMWS+m/K1y3bh1hYWEMGDDACxGJiMi5ICMjg7fffpvhw4czZswYMjMzWblyJRMmTODtt9+mTZs2Hse/++67ZGZmct1115Gdnc3y5cu56aabWLt2LbVr1wbye/Nee+21xMTEMHHiREJDQ3nvvfe47bbbmDdvHoMGDSpTrHa7nYkTJ7Jjxw6ee+45+vfv77H/5MmTXHXVVRiGwd/+9jeio6P57LPPuPfee8nIyODGG28stuyvvvqKQ4cOMXr0aOrUqcOePXtYsWIFe/fuZcWKFRiGQY8ePahfvz5r164tdA1r166lSZMmdO7cucTlFeXgwYPccMMNREZGsnTpUqKjowF48803adWqFQMGDCAwMJBPPvmEhx56CNM0+dvf/laojBkzZnDllVdy+eWXs2rVKmbNmkW7du1o1apVaZ/2UlHySURERKQacTgcvP/++wwaNIjg4OASnTNp0iRSUlKoU6cOw4YNY8aMGYSEhHg5UhER8WeRkZF8/PHHBAUFubddddVVDBkyhOXLl/PYY495HH/w4EHef/99YmJicDqddO/enXHjxrF48WJmz54NwD//+U/q16/PqlWr3OVed911XHvttTz55JNlTj7NmjWL48eP8+yzzxY5D+IzzzyD0+lk7dq17qHl1157LXfeeSfz58/nmmuuKfZz77rrruPmm2/22NapUyfuvPNOvvvuO7p27YphGFx22WW8/PLLpKenEx4eDkBycjJffvklkydPLlV5f/Xbb79x4403EhMTw5IlS4iMjHTve+211zxiHzt2LOPHj+fll18ulHzav38/r7/+uruOIUOG0K9fP1avXs3dd99d5PVXFCWfRERERKqRzz77jJSUlBINuQsPD2fChAl069aN4OBgtmzZwtKlS9m3bx8LFy4scwymaZKVlVXm86V4drvd46e3GYaBzWYjL8+Bw+GolDorQp4l/88cu91eaJ60ilTZ90POTPfjTzk5ObhcLpxOJ06ns9B+l8vl/lnU/gIBAQE4nU5cLhdpaWmYpkm7du34+eef3ecVlDVw4EBq166N0+nENE3at29Phw4d2Lx5M3//+99JSUlhy5YtTJs2jbS0NI96evfuzfz58zly5AgxMTEljq/guJMnTxIUFOROfJ3ONE3ef/99Bg8ejNPpdA9XA7jgggtYv349P/30E126dCmyXqvV6v5/Tk4OWVlZ7pX1duzY4e7RNGLECBYuXMh7773HFVdcAeT3RM7Ly2PYsGGlLq8gll27dnHXXXfRpEkTFi5cSFhYmMc1nl5eeno6eXl5dO3alS+++IKUlBTCw8PdvwdbtGhB586d3cdHRkYSGxvLwYMHi32eC+6/3W53x/TX57e43lqnU/JJREREpBopGN7Qq1evsx7btm1b2rZt637cq1cv6taty8MPP8z27dvp0KFDmWJwOBzs3LmzTOdKySQkJFRKPTabjbZt23LqVAonM9Mrpc6KYNYIh8b53/JXRiKisu6HlIzuR77AwEBycnKK3FeQTM7NzSU7O7vYMtauXcvy5ctJSEggLy/Pvb1hw4bu8wrqaNCgQaGyGjduzAcffEB2djZ79+7FNE3mzp3L3Llzi6zv999/JzIyktzc3BLFV3Ad9957L08//TQTJ05kyZIlNGvWzH1McnIyaWlpvP3227z99ttFlnP06FGys7Pd1+JwONz1pqamsmjRIjZt2kRysufq1adOnXIf16BBA9q1a8d///tfhg0bBsCaNWuIj48nJiam1OUVPN9TpkwhOjqaefPmERgYWOj5+PHHH3nxxRfZvn17oX0nT57EarW6H58eR4GwsDBSUlKKfZ5zcnLIy8tj3759Re4HPHrHFUfJJxEREZFqIjMzk08++YQxY8YQEBBQpjKGDBnCww8/zI4dO8qcfLJarbRs2bJM58qZ2e12EhISaNasGTabzev1FXybXbNmFEZoyYZx+oOo4PznJjY21us9nyrzfsiZ6X78KScnhyNHjhAcHFzkcLKChERQUFCxw83WrFnDAw88wMCBA5kwYQLR0dFYLBYWL17MoUOH3OcVDPEODAx0bzNNk5ycHPdnUUhIiLvOm266id69exdZZ6tWrQgJCXEnM84U3+nXcd555/Hiiy9y8803M2XKFF5//XXq16/vccyIESMYOXJkkeXExcUREhLivhar1equd8qUKfz444/cdNNNtGnThtDQUFwuF5MmTcJisXjEN2rUKB5//HFSUlLIzc3lp59+4h//+IfHMSUtLzAwP11zySWX8O677/LBBx9w9dVXe8R98OBBJk+eTGxsLHfffTf16tXDarXy+eefs2zZMvfzV/B78PTrKlAwt+SZnufAwECaNGlS5HD+vXv3FnueRxklOkpERERE/F7Bt8sjRozwaRyGYRAaGurTGKo7m81Wqc9xYKAVqyvv7Af6icDA/D82KysBUdn3Q85M9yM/oWCxWAgICCjyy4iChEPBMUX54IMPaNy4Mc8//7zHsKqCxwXnFZR16NAh97aCIVwJCQk0bNiQgIAA90qqQUFB9O3b96zxny2+vx7XsWNHXnjhBSZNmsSECRN44403iI6Opk6dOtSoUQPTNEtdb2pqqnuo4NSpU93HFfSu+2t8w4cP54knnuC9994jOzsbq9XKsGHD3MeUpryCWO6++24CAwN55JFHCA8P9/iM37x5M7m5ubz44os0aNDAvf3bb7/1KO/0IXV/fT4L7m1xz3NAQAAWiwWbzVZkgqokQ+4ASr98ioiIiIj4pXXr1tGkSRM6duxY5jLWr18P4J5/QkREzk0FyYjTew9u27aNH3/8scjjP/zwQ44dO+Z+vGPHDrZv386FF14IQK1atejevTtvvfUWx48fL3T+X4eglUWvXr14+umnOXjwIBMmTCAjI4OAgAAGDx7Mpk2b2L17d6nqLS4hs2zZsiK3R0dH07dvX9asWcPatWvp06ePe1W6spRX4JFHHmHw4MHMmjWLjz76qFB5p9+j9PR0Vq1adcbyfEE9n0RERET8mN1uZ/PmzQAkJiaSkZHBxo0bAejevbu7UZucnMzXX3/NxIkTiywnMTGRQYMGMWXKFPe3rTNnzqRp06a0bdvWPeH4K6+8wsUXX6zkk4jIOWDVqlV8/vnnhbaPGzeOiy66iPfff5/bbruNiy66iMOHD/Of//yHli1bFrmoRJMmTbj22mu59tprycnJ4dVXXyUqKooJEya4j3nggQe47rrrGDFiBFdddRWNGzfm5MmT/Pjjjxw9epQ1a9aU+5oGDRrEI488wj333MOtt97KSy+9xF133cXWrVu56qqrGDNmDC1btiQ1NZWff/6Zr7/+mm+++abIssLCwujWrRsvvfQSDoeDmJgYvvzySw4fPlxs/aNGjWL69OkAzJgxo9zlQX4Ppn//+9/cdttt3H777SxatIhevXrRu3dvrFYrkydP5pprriEzM5O3336bWrVqceLEiVI+c96l5JOIiIiIH0tKSirUeC14/Oqrr9KjRw8A3nvvPfLy8oodcmeapnsFogKtWrVi7dq1LF26FIfDQcOGDZk8eTKTJk3y0tWIiIg/efPNN4vcPnr0aEaPHs3Jkyd56623+OKLL2jZsiX//ve/2bhxY5HJmlGjRmGxWFi2bBlJSUm0a9eO+++/n7p167qPadmyJatWrWL+/Pm88847pKSkEB0dTdu2bbntttsq7LquuOIKUlNTmTNnDjNmzGD+/Pm8/fbbPP/883zwwQe8+eabREVF0bJlS2bOnHnGsp566ikeeeQR3njjDUzTpHfv3ixevLjYIXz9+/cnMjISl8vFwIEDy11eAavVyty5c5k4cSJTpkzhlVdeoWPHjsydO5dnn32WOXPmULt2ba699lqio6O55557Sv6EVQLD9OYMfFXETz/9BKh7uYh4z9NfriMxrfxdiUWqs4YR0dzZe7jX69Hnvnfp+fWurKwsdu7c6Z6ktrJ89vseUnOrzvL1kUE2Lqzfyuv1+Op+SNF0P/6UnZ3N/v37iY2NPeNE0t7idDrJzs4mJCSkzAtgVFV5eXn07duX/v3789hjj/k6HKB89+Nsr6WSfu775ZxP77zzDqNGjSI+Pp4ePXowYcIEj2X/Pv74Yy677DLi4+MZPHiwX45nFBEREREREZFzy4cffkhycjKjRo3ydSh+xe+G3S1YsIDFixczefJkOnXqxKlTp/j666/ds7N/++23TJ06lSuvvJJ77rmHLVu2cO+991KjRg0uvfRSH0cvIiIiIiIiIueabdu2sWvXLl544QXatm1L9+7dfR2SX/Gr5NO+ffuYP38+L7zwAv369XNvHzx4sPv/CxYsoEOHDjz88MMA9OzZk0OHDjF37lwln0RERERERESk0r355pusWbOG8847j3/961++Dsfv+NWwu9WrV9OoUSOPxNPpcnNz2bp1a6Ek09ChQ/ntt9/OOkO8iIiIiIiIiEhF+9e//sUvv/zC6tWrad26ta/D8Tt+lXzatm0brVu35oUXXqBXr160b9+ea665hm3btgFw8OBBHA4HzZs39zivRYsWQH7PKRERERERERER8R9+NezuxIkT7Nixg927d/PAAw9gs9l48cUXufnmm3n//fdJTU0FICIiwuO8gscF+8vCNE2ysrLKHryISBEMw8Bms/k6DJEqxW63483FeE3TxDAMr5UvIiIiIp78KvlUkAB67rnnOO+88wDo2LEjAwYM4LXXXqNPnz5eq9vhcLBz506vlS8i5yabzUbbtm19HYZIlbJ//37sdu8u6R4UFOTV8kVERETkT36VfIqIiCAqKsqdeAKIioqibdu27N27l2HDhgGQnp7ucV5aWhoAkZGRZa7barXSsmXLMp8vIlIU9a4QKb3Y2Fiv9nzau3ev18oWERERkcL8KvnUsmVLDh48WOS+nJwcmjRpgtVqZd++ffTt29e9r2Cup7/OBVUahmEQGhpa5vNFRESkYnh7qKqSwiIiIiKVy68mHO/fvz8pKSkew99OnTrFzz//TLt27QgKCqJHjx5s2rTJ47wNGzbQokULGjVqVNkhi4iIiIiIiIjIGfhVz6eLL76Y+Ph4pk+fzh133EFwcDCLFi0iKCiI6667DoBbb72VcePG8eCDDzJkyBC2bt3KunXreOaZZ3wcvYiIiIiIiIiI/JVf9XyyWCwsWrSITp06cf/993PnnXcSFhbG66+/Tp06dQDo2rUr8+bN47vvvmP8+PGsW7eORx99lCFDhvg4ehERERERERGB/L/vK8q8efPo3LlzhZVXHqtXryYuLo7k5GRfh1Kl+FXPJ4Do6Gj+/e9/n/GYgQMHMnDgwEqKSERERERERKT6ME3Tq3MgBgQEFDmHo7frFf/ld8knEREREREREfEewzD4/uRBMhw5Xinf5TJxOvMICAjEYslPNoVZg+lSu4lX6hP/51fD7kRERERERETE+zIcOaTm2r32LyXH87G3El0ATz75JCNGjKBz58707duXO++8k+PHj7v3L1++nI4dO5KRkeFx3m+//UZcXBybN28G4NNPP+Wmm26iV69edOnShTFjxvDZZ5+dtf5Vq1bRvn173n777Yq9sGpEyScRERERERERqbKSkpK45ZZbWLhwIffeey+JiYlcf/315OXlAXDZZZdhmibr1q3zOG/lypXExMTQp08fAA4fPkz//v154oknmDdvHl26dGHSpEls3bq12LqXL1/OAw88wJw5cxgzZoz3LrKK07A7EREREREREamyHn/8cff/nU4nnTt35sILL2TLli306dOHyMhIBg8ezKpVq7jmmmsAyMvLY82aNVx55ZUEBAQAMHbsWHc5LpeLHj16sHfvXlasWEGPHj0K1btw4ULmz5/Pc889p3mpz0LJJxERERERERGpsjZv3syCBQvYs2ePx9C6hIQEd6+mq666irFjx7Jnzx5atWrF5s2bSUpK4oorrnAff/ToUZ555hm++uorTpw4gWmaALRr165Qnc8++yxr165l4cKFXHDBBV6+wqpPyScRERERERERqZK2b9/OlClTGDhwIBMnTqRWrVoYhsFVV11FTs6f80x169aN2NhYVq5cyezZs1m1ahXdunWjSZP8SdBdLhe33nor6enpTJ8+naZNm2Kz2Zg7dy6///57oXo3bdpE69atOf/88yvtWqsyzfkkIiIiIiIiIlXShx9+SFhYGM8++ywDBw6kU6dO1K5du8hjx4wZw5o1azh69CibN2/26PV04MABfvnlF2bNmsWYMWPo3r078fHxZGdnF1nWCy+8wKFDh5g2bRoOh8Mr11adKPkkIiIiIiIiIlVSdnY2VqsVwzDc29auXVvksZdffjnp6enMnDmTkJAQLr30Uve+gl5SVqvVvS0xMZEffvihyLJiY2N5+eWX2bZtG3fddRdOp7MiLqfa0rA7EREREREREfFrTqeTjRs3Ftretm1bli1bxiOPPMKgQYP44Ycf+O9//1tkGdHR0QwcOJCNGzdy9dVXExIS4t7XvHlz6tWrx1NPPYXL5SIrK4u5c+dSt27dYmOKi4tj6dKl3HDDDdx999088cQTWCzq41MUJZ9EREREREREzjFh1mCvle1ymTgD8ggICMRiMSqkvpycHGbMmFFo+xNPPMHMmTN57bXXWL16NV26dGHhwoUMHjy4yHIGDRrExo0bufLKKz22BwUFMW/ePB5++GFmzJhB/fr1ufXWW9myZQs7duwoNq527drx0ksvcdNNN3H//ffzyCOPePTCknxKPomIiIiIiIicQ0zTpEvtJj6ptyyJmWnTpjFt2rQzHjNx4kSPx7t27SryuM8++4zWrVvToUOHQvs6dOjAypUrPbaNGjXK4/Ho0aMZPXq0x7ZOnToVOzxP8in5JCIiIiIiInIO8XbPHKfTSW5uLkFBQQQEBFRavWeya9cudu7cyYYNG3jggQd8Fse5SsknERERET924MABlixZwrZt29izZw/Nmzdn3bp1Hsdcf/31fPPNN4XO3bBhAy1atDhj+ceOHePRRx/liy++wGq1MmjQIGbPnk1YWFiFXoeIiJxbXC6Xr0PwcOutt5KcnMyoUaM8VrmTyqHkk4iIiIgf27NnD5s3b6Zjx464XC5M0yzyuC5dunD33Xd7bGvUqNEZy3Y4HEyYMAGAp556iuzsbObMmcNdd93FwoULK+YCRERE/MDHH3/s6xDOaUo+iYiIiPixAQMGcPHFFwMwa9asYic9jYiIoFOnTqUqe9OmTezZs4cNGzbQvHlzdznjx49n+/btRc6HISIiIlJaWgNQRERExI95c8nmzz77jLi4OHfiCaB3795ERUWxefNmr9UrIiIi5xYln0RERESqgW+++YZOnToRHx/P2LFj+d///nfWc/bt2+eReIL8yWBjY2PZt2+ft0IVERGRc4yG3YmIiIhUcd26dWPkyJE0a9aM48ePs2TJEm666SaWL19O586diz0vLS2N8PDwQtsjIyNJTU0tczymaZKVlVXm86V4drvd46e3GYaBzWYjL8+Bw+GolDorQp4l/88cu91e7DxpFaGy74ecme7Hn3JycnC5XDidTpxOZ6XXX/C+M03TJ/WLp/LcD6fTicvlwm63FzmJvGmaJVrFUMknERERkSpu+vTpHo8vuugihg8fzgsvvMDixYsrPR6Hw8HOnTsrvd5zSUJCQqXUY7PZaNu2LadOpXAyM71S6qwIZo1waAz79++vlEREZd0PKRndj3yBgYHk5OT4NAZf1y+eynI/cnJyyMvLO2OP6KCgoLOWo+STiIiISDUTGhpKv3792LRp0xmPi4iIICMjo9D21NRU6tevX+b6rVYrLVu2LPP5Ujy73U5CQgLNmjXDZrN5vb6Cb7Nr1ozCCA32en0VJSo4/7mJjY31es+nyrwfcma6H3/KycnhyJEjBAcHExISUun1m6ZJTk4OwcHBJeoVI95V3vsRGBhIkyZNCA4u/Dmwd+/ekpVR6lpFREREpFpo3rw5u3fv9thmmib79++nd+/eZS7XMAxCQ0PLG56cgc1mq9TnODDQitWVV2n1lVdgoBWg0hIQlX0/5Mx0P/IXq7BYLAQEBBAQEFDp9RcM7TIMo9z1x8XFnfWYxx9/nHfeeYfQ0FAWLlx4xmO7du3KDTfcwLRp00ocw+rVq5k9ezZff/010dHRJT5v3rx5LF26lB9++KHE53hDwf149913uffee0t1HQEBAVgsFmw2W5GJzJIms5R8EhEREalmsrKy+PTTT4mPjz/jcRdeeCFr1qxx9xQA+Prrr0lJSaFfv36VEKmIiFRXFbVa61tvveXx+Oqrr+b6669n+PDh7m1NmjShQ4cOXl0hVspHyScRERERP2a329m8eTMAiYmJZGRksHHjRgC6d+/Ovn37eOmllxg0aBANGzbk+PHjvPzyy5w4cYLnnnvOXU5iYiKDBg1iypQpTJ06FYDBgwezcOFCpk2bxp133ondbueJJ57goosuokOHDpV/sSIiUilKOkl0WQUEBBTZ87As9Xbq1KnQtvr16xfaXpoeSVL5lHwSERER8WNJSUnMmDHDY1vB41dffZV69erhcDh45plnSElJwWaz0blzZx566CGPBFLBCjenz39jtVp56aWXePTRR7nzzjsJDAxk0KBB3HPPPZVzcSIi4hOGYeDa/R3YvbOQgGnmr7QXEBCAYfzRG8kWjqX1+V6pD+D6668vNOzuww8/5MknnyQxMZG4uDjuv//+Qud9+umnLFu2jF9//ZWcnBxatGjBtGnTuPDCC89Y36pVq3jggQd44IEHGDNmTJnjfvLJJ9m8eTOHDx8mLCyMbt26MWvWLOrWrQvA8uXLefLJJ/nyyy8JCwtzn/fbb78xdOhQFi1aRL9+/Xx+HWej5JOIiIiIH2vUqBG7du064zFLliwpczkxMTHMmzevzPGJiEgVZU+HzFTvlG26MPPyIDAQDN8Mhdu5cyfTp0/nwgsvZPbs2Rw+fJjbb7+d3Nxcj+MOHz5M//79ufnmm7FYLHz22WdMmjSJZcuW0aNHjyLLXr58OXPmzGHOnDkMGzasXHEmJSVxyy23ULduXZKTk3n55Ze5/vrrWb9+PYGBgVx22WX8+9//Zt26dVxzzTXu81auXElMTAx9+vQ563V07drV69dxNko+iYiIiIiIiEi1smjRIurXr8/zzz/vnvQ8ODiYe++91+O4sWPHuv/vcrno0aMHe/fuZcWKFUUmnxYuXMj8+fN57rnnGDhwYLnjfPzxx93/dzqddO7cmQsvvJAtW7bQp08fIiMjGTx4MKtWrXInn/Ly8lizZg1XXnml+9rOdB1FJZ8q+jrORsknEREREREREalWtm3bxoABAzxW27v00ksLJZ+OHj3KM888w1dffcWJEyfcw9PbtWtXqMxnn32WtWvXsnDhQi644IIKiXPz5s0sWLCAPXv2kJGR4d6ekJDg7tV01VVXMXbsWPbs2UOrVq3YvHkzSUlJXHHFFX5zHWej5JOIiIiIiIiIVCsnTpygVq1aHtvCwsIIDg52P3a5XNx6662kp6czffp0mjZtis1mY+7cufz++++Fyty0aROtW7fm/PMrZu6q7du3M2XKFAYOHMjEiROpVasWhmFw1VVXkZOT4z6uW7duxMbGsnLlSmbPns2qVavo1q0bTZo08YvrKAmtQygiIiIiIiIi1UqdOnVISkry2JaRkeGR1Dlw4AC//PILs2bNYsyYMXTv3p34+Hiys7OLLPOFF17g0KFDTJs2DYfDUe4YP/zwQ8LCwnj22WcZOHAgnTp1onbt2kUeO2bMGNasWcPRo0fZvHmzR68nX19HSSj5JCIiIiIiIiLVSocOHfjkk09wOp3ubRs3bvQ4piARZbVa3dsSExP54YcfiiwzNjaWl19+mW3btnHXXXd5lF0W2dnZWK1WDMNwb1u7dm2Rx15++eWkp6czc+ZMQkJCuPTSS/3mOkrCr4bdrV69mtmzZxfaPnHiRGbOnOl+/Pbbb/PSSy9x5MgRYmNjueOOO+jfv39lhioiIiIiIiIifmrSpElceeWV3HbbbVx77bUcPnyYJUuWeAy7a968OfXq1eOpp57C5XKRlZXF3LlzqVu3brHlxsXFsXTpUm644QbuvvtunnjiCSyW4vv1OJ3OQkkvyE+O9e7dm2XLlvHII48waNAgfvjhB/773/8WWU50dDQDBw5k48aNXH311YSEhFTqdZSXXyWfCrz00kuEh4e7H8fExLj/v379eu677z4mT55Mz5492bBhA1OnTuX111+nU6dOPohWREREREREpIqxhZ/9mLIyXRhOJwQEgGHxfn1FaNu2Lc899xxPPvkkU6dOpVWrVjzzzDOMHz/efUxQUBDz5s3j4YcfZsaMGdSvX59bb72VLVu2sGPHjmLLbteuHS+99BI33XQT999/P4888ohH76XT5eTkMGPGjELbn3jiCUaOHMnMmTN57bXXWL16NV26dGHhwoUMHjy4yLIGDRrExo0bufLKKz22V8Z1lJdhFkyB7gcKej59/fXXREdHF3nM4MGDad++PU899ZR72zXXXEN4eDiLFy8uU70//fQTAPHx8WU6X0TkbJ7+ch2Jacm+DkPErzWMiObO3sO9Xo8+971Lz693ZWVlsXPnTtq0aUNoaGil1fvZ73tIzbVXWn3lFRlk48L6rbxej6/uhxRN9+NP2dnZ7N+/n9jYWI8eMgVM0/RakuFMfFVvdfH3v/+dnTt3Fjs0rzhOp5Ps7GxCQkI8Vv8ribO9lkr6uV+l5nw6dOgQCQkJDBkyxGP70KFD+frrr8nNzfVRZCIiIiIiIiJVg7cTQE6nE7vdXmguISWeymbXrl28++67bNiwgXHjxvk6nDLxy2F3w4cP59SpUzRo0ICrrrqKCRMmEBAQwL59+4D8ybFO16JFCxwOB4cOHaJFixa+CFlERERERERE/uByuXwdQrVx6623kpyczKhRozxWuatK/Cr5VKdOHaZNm0bHjh0xDIOPP/6YZ599lmPHjnH//feTmpoKQEREhMd5BY8L9peFaZpkZWWVPXgRkSIYhoHNZvN1GCJVit1ux5uzAqjLv4iIiFQlH3/8sa9DKDe/Sj717duXvn37uh/36dOH4OBgli1bxuTJk71at8PhYOfOnV6tQ0TOPTabjbZt2/o6DJEqZf/+/djt3p1bJigoyKvli4iIiMif/Cr5VJQhQ4awdOlSdu7cSWRkJADp6enUqVPHfUxaWhqAe39ZWK1WWrZsWb5gRUT+Qr0rREovNjbWqz2f9u7d67WyRURERKQwv08+na558+YA7Nu3z/3/gsdWq5XGjRuXuWzDMM75FRFERET8gbeHqiopLCIi5wo/WtxeqqiKeg35/Wp3GzZsICAggLZt29K4cWOaNWvGxo0bCx3Tq1cvdaEXERERERGRc57VagXQvMZSbgWvoYLXVFn5Vc+n8ePH06NHD+Li4gD46KOPWLFiBePGjXMPs5s2bRozZ86kSZMm9OjRgw0bNrB9+3Zee+01X4YuIiIiIiIi4hcCAgKIiori+PHjAISGhlZqz1+n00lOTo47FvGtstyPgkXZjh8/TlRUVLnvo18ln2JjY1m1ahVHjx7F5XLRrFkz7rnnHq6//nr3McOHD8dut7N48WIWLVpEbGws8+fPp3Pnzj6MXERERERERMR/1KtXD8CdgKpMLpeLvLw8AgMDsVj8fsBVtVee+xEVFeV+LZWHXyWf/vGPf5TouDFjxjBmzBgvRyMiIiIiIiJSNRmGQf369albty4Oh6NS67bb7ezbt48mTZp4fS5HObuy3g+r1VphPdf8KvkkIiIiIiIiIhUnICCg0oe+uVwuAIKDgwkJCanUuqUwf7gf6v8mIiIiIiIiIiJeo+STiIiIiIiIiIh4jZJPIiIiIiIiIiLiNUo+iYiIiIiIiIiI1yj5JCIiIiIiIiIVxjAMbDYbhmH4OhTxE1rtTkRERERERKQKMk3TLxM8NpuNtm3bFrvfX+MW71HySURERERERKQKMgwD1+7vwJ7u61A85OXlkZJyiqiomgQG/iXtYAvH0vp83wQmPqPkk4iIiIgfO3DgAEuWLGHbtm3s2bOH5s2bs27dOvf+jIwMXn75ZTZv3kxCQgJBQUF06NCBO+64g7i4uDOWvXXrVsaNG1do+9ChQ3nmmWcq/FpEystpunCaJqZpEmixEGBoFhER7OmQmerrKDyYDgeOlJOYgQZYrb4OR/yAkk8iIiIifmzPnj1s3ryZjh074nK5ME3TY/+RI0d46623uOKKK7j99tvJyclh6dKlXH311axatYoWLVqctY7HH3+c5s2bux/XrFmzwq9DpCxM0+R4dgbHstJIzs0iw5Hj3mcANQKDiQwKoUGNSOqEhGPRMB4REb+k5JOIiIiIHxswYAAXX3wxALNmzWLHjh0e+xs1asQHH3yAzWZzb+vZsycDBgzgjTfe4L777jtrHa1atSI+Pr5iAxcpB9M0OZhxin3pJ8nMyy36GCAjL4eMvBwSs1IJsgTQPLw2seG1KjdYERE5KyWfRERERPyYxXLmYUWhoaGFttWoUYMmTZpw/Phxb4Ul4jVpudlsT04kJdcOQKBhoVGNKGqHhBEVbCPIEogBZDvzSHdkcyI7g8TMFHJdTn5NPUZCRhJdajehb72WmtBYRMRPKPkkIiIiUs2kpaWxZ88eLrjgghIdP2nSJFJSUqhTpw7Dhg1jxowZhISEeDlKkcIS0pP4+dRRTEwCDQutI+vSJKwmgZaAQsfaAq3YAq3UtYXTJqoeR7JS2ZVyDLvTwVfH9uFwObmuZTdCA4N8cCUiInI6JZ9EREREqpl///vfGIbBtddee8bjwsPDmTBhAt26dSM4OJgtW7awdOlS9u3bx8KFC8tcv2maZGVllfl8KZ7dbvf46W2GYWCz2cjLc+BwOLxWj2ma7Eo/wYHMUwDUCQ6jbWRdQgKsmE4XDqfrrGXEBNWgdp1mJGQk81tGEv87cYDfUk8woWVP6tsivBJ3Zd8PObNz7X78+f7Mw/Ti+7Ms8vIcHj9PZ+TlEUT+ffrrPIbiHd58b5imWaJepko+iYiIiFQjq1atYsWKFfzrX/+iXr16Zzy2bdu2tG3b1v24V69e1K1bl4cffpjt27fToUOHMsXgcDjYuXNnmc6VkklISKiUemw2G23btuXUqRROZnpnKXfTNElwZXDKzJ/bqYHFRkxeEOnJqZSlxgigW4367HOmczI7g2d/+ZRLghvSIKDwENWKUln3Q0rmXLkfBe/PlJRTOFJO+jqcIqWkpBTaZs0zqQvs37//nEkU+gtvvTeCgs7ew1TJJxEREZFqYvPmzdx///1MmTKFyy+/vExlDBkyhIcffpgdO3aUOflktVpp2bJlmc6VM7Pb7SQkJNCsWTOPSea9peDb7Jo1ozBCgyu8fNM0+Tn1GKfsuRgYxEfVq5BeSlHBNq6pdwHzd3zKvowk3stNZFxsVzrUbFABUf+psu+HnNm5dj8K3p9RUTUxA/1rfrO8PAcpKSlERUURGGj12GeERQEQGxurnk+VxJvvjb1795boOCWfRERERKqBH3/8kRkzZjBq1ChmzJjh01gMwyhyInSpODabrVKf48BAK1ZXXoWX+8up30m0pwLQpXYj6odGVki5gYFWaliDubPjxSzd9RXfnzzEq/u/5VZbX+KjG1ZIHaer7PshZ3au3Y/AwECwWs9+oA8EBlqx/jW2wPw0xLmQIPQ33nhvlHRhhzMvnyIiIiIifm/v3r3ccsst9OzZk4ceeqhcZa1fvx6A+Pj4ighNpFgHM5LZl54EQMfohhWWeDqd1RLAxPN607V2E5ymixd/+Zydp45WeD0iInJm6vkkIiIi4sfsdjubN28GIDExkYyMDDZu3AhA9+7dMU2T8ePHExwczA033MCOHTvc54aFhbmHvyUmJjJo0CCmTJnC1KlTAZg5cyZNmzalbdu27gnHX3nlFS6++GIln8SrUnLs7Ej+HYC4yLo0DqvptboshoWb4y7AYbrYlnSYBTs/4+6Ol9CwRpTX6hQREU9KPomIiIj4saSkpELD6Aoev/rqqwAcPZrfk+PGG2/0OK579+4sX74cyJ9bx+l0esyv0apVK9auXcvSpUtxOBw0bNiQyZMnM2nSJG9djgi5zjy+O3kQFyYxtnBaRtTxep0BFgsTz+vN3B2fsDv1OM//vJlZnS4hIkjDfkREKoOSTyIiIiJ+rFGjRuzateuMx5xtf3Hl3HLLLdxyyy3lik+kNEzT5KfkI9idDkIDg+hUq1GJ5wspL6slgMlt+vKvbe9z3J7OC798xl0dLsZqCShzmQVL3VfWNYiIVFWa80lERERERCrFkaxUfrenYQDn125crsTPmQRbAotcRauGNZipbfsRGhjE/vQkVu77vlz1FCx1X9ETJ2sFsMql51vE+9TzSUREREREvC7b6WDHqfx5nlpF1iXSi0PerJYADMPg+5MHyXDkFNrfvU5TPv19D5/+vgeHy0mz8Fplqicvz8GpUynUrFl4OfmyCrMG06V2kwopS0rGMAxcu78De7qvQymdqLpYmrb1dRQiJaLkk4iIiIiIeN2O5N9xuJxEWEMqZZ4ngAxHDqm59kLbw6zBtIyozd60k2w9noDVEkCYNbjU5TscDk5mpmOEBmN15VVEyOIr9nTITPV1FKVjC/N1BCIlpmF3IiIiIiLiVcft6Rz9Y7hdp1qNsPjBHEmtI2OIDg4lz3TxY9JhXBp6JSLiNUo+iYiIiIiI1zhNl3u4XWx4LSKCQnwcUT6LYdC5VmMCDQspuXb2pp3wdUgiItWWkk8iIiIiIuI1+9OSyMrLJdgSSKvIur4Ox4Mt0Er76AYA7Ek9TkoRQ/RERKT8lHwSERERERGvyM5zsCftOABtatbz2up25dEwNJL6tghMYFvSYVymy9chiYhUO0o+iYiIiIiIV+xOO47TNKkZFErD0Ehfh1MkwzCIj25AkCWAdEcOv6Wd9HVIIiLVjt8mnzIzM7nwwguJi4vjp59+8tj39ttvM3jwYOLj47nsssv45JNPfBSliIiIiIgUJcORw6GMUwC0qRmD4QeTjBcnKCCQdjXrA7An9QQZjhwfRyQiUr34bfLphRdewOl0Ftq+fv167rvvPoYMGcLixYvp1KkTU6dO5ccff6z8IEVEREREpEi7Uo5hAjG2cKKDa/g6nLNqEBpJnZAwXJhsT07E1Op3IiIVxi+TT7/99htvvPEG06ZNK7Rv7ty5DBs2jNtvv52ePXvy8MMPEx8fz/PPP++DSEVERERE5K9ScrL43Z4GQFxkjI+jKZmC4XcBhkFyThaJmSm+DklEpNrwy+TTo48+yjXXXENsbKzH9kOHDpGQkMCQIUM8tg8dOpSvv/6a3NzcygxTRERERESKsDs1f5LxRjWiiAgK8XE0JRcaGORekW9nyjEcrsIjMUREpPT8Lvm0ceNGdu/ezW233VZo3759+wAKJaVatGiBw+Hg0KFDlRKjiIiIiIgULSXXzvHsDABaRdTxcTSl1zy8FjUCg8hx5bmTaCIiUj6BZTnJ6XTyxRdfcOjQIVJTUwuNhzYMo8jk0dnY7Xb+9a9/cccddxAWFlZof2pqKgAREREe2wseF+wvC9M0ycrKKvP5IiJFMQwDm83m6zBEqhS73e7VuVZM0/TKxMfeah+JVDV7/kjYNAyNpIY12MfRlJ7FsNC+Zn22njhAQnoSjWvUrFK9t0RE/FGpk08//fQT06dP5+jRo8U2DMvauFqwYAG1atXiiiuuKPW55eVwONi5c2el1ysi1ZvNZqNt27a+DkOkStm/fz92u92rdQQFBVVoed5sH4lUJWm52RyzpwO4h69VRXVs4dSzRXDUnsbOlKP0qNvM1yGJiFRppU4+PfTQQ2RnZ/P888/TtWvXQr2QyioxMZGlS5fy/PPPk56e/4FV0BMpKyuLzMxMIiMjAUhPT6dOnT+78Kal5U9mWLC/LKxWKy1btizz+SIiRfHnZaVF/FVsbKxXez7t3bu3wsv0VvtI5K8KetT66+fLnrT8Xk8NQiMJq4K9nk7XJiqGY/Y0TmRncDI7g9ohhUdmiH/x9/eHyLms1MmnXbt2cccddzBgwIAKDeTw4cM4HA4mTZpUaN+4cePo2LEjTz31FJA/91Pz5s3d+/ft24fVaqVx48Zlrt8wDEJDQ8t8voiIiFQMbw9V9cYfJd5qH0n1UhFDPv25R21mXi6/Z+V/KdyyCs719Fc1rME0DYsmISOZnaeO0qdeCyU1zsJbw5pLyp/fHyLnulInn+rVq+eVbyPbtGnDq6++6rFt586dPP744zz00EPEx8fTuHFjmjVrxsaNG7n44ovdx23YsIFevXpVeBd6ERERkZLwVvtIqhfDMHDt/g7+GJZWFnl5eaSknCIqqiaBgWWavrV0oupiaVqyP+b3p50EoE5IWLWZI6lVZF0OZ6aQ6sjmSFYqDWtE+Tokv1YRr/HyKNP7oxSvcREpu1J/Yk2cOJElS5Zw9dVXFzkpeFlFRETQo0ePIve1a9eOdu3aATBt2jRmzpxJkyZN6NGjBxs2bGD79u289tprFRaLiIiISGl4q30k1ZA9HTLLsUiOw4Ej5SRmoAFWawUGVgxbyV7Puc48DmWeAqBFRG1vRlSpggMCaRFRm12px/k15Rj1QiMIMPxuwXD/Us7XeHmU6f1Rwte4iJTPWZNPL7/8cqFtNWrUYNCgQQwbNox69eoREBDgsd8wDG688cYKC/J0w4cPx263s3jxYhYtWkRsbCzz58+nc+fOXqlPRERE5K/8rX0k4msHMpJxmiYR1hBqBdfwdTgVKja8NgkZydidDg6kJ9O8GiXXREQqy1mTT3PmzCl2X3G9jSqqcdWjRw927dpVaPuYMWMYM2ZMucsXERERKQtfto9E/I3TdJGQngxA84ja1W5epECLhdaRdfkp+Qh70k7QOKwmVkvA2U8UERG3syafPvroo8qIQ0RERKTKUPtI5E+/Z6WS48ojJCCQBqFlX33anzWuUZP9aUlk5OWwN+0EbaLq+TokEZEq5azJp4YNG1ZGHCIiIiJVhtpHIn/a/0evp6Zh0ViqWa+nAhbD4LyoGL49eZD96UnEhtUiJLAS5twSEakmKmGJDBERERERqY5O5WSRmmvHgkGTsGhfh+NVMbZwagaHcioni71pJ2kfXd/XIck5yHTmgSMbcnMgLxeXaWL+/hvYM8F0gWGAJQACAv/8GRQCQSEYmixffKhMyadff/2V1157jV9++YX09HRcLpfHfsMw+PDDDyskQBEREZGqQO0jORclpCcB0KBGJMEB1ft7bcMwaB1Zl63HEziYkUzLiNpo5ifxBtPlguwMyErPXz0wKw3sGflJJ2ee57GlKTcoBIJsEFIDQiPy/9WIxLAGV+wFiBSh1J8QW7duZcKECURGRtK+fXt++eUXevbsSU5ODj/++CMtW7akffv23ohVRERExC95s3104MABlixZwrZt29izZw/Nmzdn3bp1hY57++23eemllzhy5AixsbHccccd9O/f/6zlHzt2jEcffZQvvvgCq9XKoEGDmD17NmFhWn5czizb6eBIVhoAzcJq+TiaylE7uIa799Nv6SdpHaaV76T8TGcepCfn/0tLgoxT+b2YimMJyO/NFBIGYVH5SaqCXk+mCa48cDrB5YQ8R37SyjQhNzv/X8Ypz/qtwRAeDRG18//ZwqrdwgHie6VOPs2dO5fGjRuzYsUKcnNzueCCC7jlllvo1asX27ZtY+LEicycOdMbsYqIiIj4JW+2j/bs2cPmzZvp2LEjLpcL0yz8Pff69eu57777mDx5Mj179mTDhg1MnTqV119/nU6dOhVbtsPhYMKECQA89dRTZGdnM2fOHO666y4WLlxYpnjl3HEo4xQmJlFBNqKCbb4Op1IYhkHriLpsPZHAgYxkmtqifB2SVFFmdgYkH4VTRyH9FIX6MAUEgi08v3eSLRxCw/N7LQWFYBT0MqzdEEvrrri2fQqZqcXXZZrgyIFcO+TY83tVZabm96jKzszfl/x7/j8AazBmRG2oWQ9qxvxZn0g5lPpV9MsvvzBt2jTCwsJITc1/gRd0K+/YsSNXX301zz33HP369avYSEVERET8lDfbRwMGDODiiy8GYNasWezYsaPQMXPnzmXYsGHcfvvtAPTs2ZPdu3fz/PPPs3jx4mLL3rRpE3v27GHDhg00b94cgIiICMaPH8/27dvp0KFDqeOVc4Npmhz6o/dE02o+19Nf1Q6pQc2gUE7lZrE/M5laaB4dKRkzKx2SDucnnezpnjuDbBBRC8Jr5f8MqVFhvY8Mw3DP+0RYTc+YnHn5iaj0JEg9md/7ypEDSYn5/wwLZlQdiK4PNethBAZVSExy7il18ikgIIAaNWoA+Y2TwMBAkpKS3PsbN27Mb7/9VnERioiIiPg5b7aPLJYz/2F76NAhEhIS+L//+z+P7UOHDuWJJ54gNzeXoKCi/1j47LPPiIuLcyeeAHr37k1UVBSbN29W8kmKdTI7kyyng0DDQoPQSF+HU6nccz+dSOBQZgoRAVG+Dkn8mJnnyE/iHD8ImSl/7jCM/ERTdH2IisEICfVJfEZAYH6yK6IWNGyN6XJCRgqkHMvvCZWdCaeO5f8zLJjR9aFuU4iopaF5UiqlTj41adKEhIQEIP8Xb/Pmzfnwww+57LLLAPj000+pXVtjn0VEROTc4cv20b59+wCIjY312N6iRQscDgeHDh2iRYsWxZ57euIJ8uOPjY11lytSlIOZyQA0rBFFwFkSpNVRfu8nG6dy7Rxz2Wng64DE75gZKXD0N0j6/bT5mwyoGQO1GkBUXb/sRWRYAtzJKLNxm/weWklH8hNR9vQ/e0QFh2LWbQp1m2jCcimRUief+vXrx6pVq7jrrrsIDAzkpptuYvbs2VxyySUAHDx4kDvvvLPCAxURERHxV75sHxUM84uIiPDYXvC4YH9R0tLSCA8PL7Q9MjLyjOedjWmaZGVllfn86sgwDGw2G3l5eZgOR5nLyctzePz0NkueEwuQl5eH44+4c515HP1jovEGIeHu7f4k748VwU6Pu6I1D6vFd8mHOWFmk5GTTUVN0Z9nyf8TzW63FznHm7+qqNd4eZTl/VHwGnc683CVN27TxEg9juXYfozTJvU2Q8Jw1W6EGd0AChI1JlDO+io09uJYbVCvBcQ0h6w0LCcPYSQfwcjJgkM7MQ/vwlWrIa6Y2PxV9P5wpnth5OURRNV7jVdldrvd42dFMk2zRL3gSp18mjJlCuPGjSMgIH9h0csvvxyLxcL7779PQEAAkydPZvTo0aWPWERERKSKUvvIk8PhYOfOnb4Ow6/YbDbatm1LSsopHCkny11eSkpK+YMqAZstkmggPT2dE6n5f0wfc9kxgVACyEnJ4AQZlRJLaUSagVDPM+6KZpomNQgkkzz2nDpGo4AaZz+pJOXWCIfGsH//fq/8oegtFf0aL4/SvD8KXuNpaelkJ50oW4WmC1tGEjXSjhKQl5O/CYPsGtFkhdfBEVQjf5hdalrZyi9GhcReWjViMGy1Cck6hS39BEG5mRgnD2GcPESOLYrMiHo4Qv5MxRZ1L6x5JnWpeq/x6qCgl3ZFK254/+lKnXyyWq3UrOk5SdnIkSMZOXJkaYsSERERqRZ82T6KjMyfbyc9PZ06deq4t6elpXnsL0pERAQZGYUTB6mpqdSvX7/MMVmtVlq2bFnm86ujgm+Fo6JqYgaWfZ6UvDwHKSkpREVFERhorajwimUJy+9BFx4ejjMoANM02XUiAYBmkbWpExrl9RjKIjwsv0dfQdze4soMZlva75w0c2hXqxFBlvLXVbByYGxsbJXqFVJRr/HyKMv7o+A1HhERTpjhLF2Fpgsj6QiWo3sxcvOTKGZAIGadJrjqNMUaFII3Z0QrV+zlVg/M88jLOIXl2H4sqccJsacQYk/BFV4LR73mnMo1i7wXRlgUUPVe41WZ3W4nISGBZs2aYbNV7Oqke/fuLdFxWjNRREREpAormLPpr/M37du3D6vVSuPGjc947u7duz22mabJ/v376d27d5ljMgyD0FDfTJ7r7wIDA8Fa/qRRYKAVawWUc/aKAv6oLxCry0pSdiaZzlwCDIPG4dFYKyDZ4g2BfywNXxC3t8SEhmNLO44dJ4nZabSOrFvuMgv+UK/oPxArS0W9xssXQyneH3+8xgMCAgko4TmmaebPe3R4V/6E3JA/nK5BS4y6TbEEBFIp74wyxF7homMgOgbTng5HfoOTh7CkJxGcnkSULYrA0HZYbX/5PAjMf39W1dd4VWaz2Sr887mkE8+fNfk0bty4MlW+bNmyUp8nIiIiUhX4U/uocePGNGvWjI0bN3LxxRe7t2/YsIFevXqdsSv8hRdeyJo1a9zfhgJ8/fXXpKSk0K9fvwqPVaq+g3/MY9MgNNJvE0+VyTAM6lls7HdlsD89iebhtQk8BydgP5eY6cmQ8BNk/jEvXmAQNGgJMc3yV447Rxm2cGjRCbNhK0jcjXniECH2FMydX2LWaghN2mAE60uJc9lZ3x1l6QanrnMiIiJSnVVm+8hut7N582YAEhMTycjIYOPGjQB0796d6Ohopk2bxsyZM2nSpAk9evRgw4YNbN++nddee81dTmJiIoMGDWLKlClMnToVgMGDB7Nw4UKmTZvGnXfeid1u54knnuCiiy6iQ4cOZYpXqq9cl5Pf7fl/cDcJi/ZxNP4jygjCFmDF7nRwKPMUseG1fB2SeIGZY4eDv+T3eAIICIT6LaF+83M66fRXRkgNaNGZvLrNcOzfgS3rVP5zduooZoOWUL8FvhmUKb521nfJ8uXLKyOOc4bLdGEx9G2IyNnovSIi/qwy20dJSUnMmDHDY1vB41dffZUePXowfPhw7HY7ixcvZtGiRcTGxjJ//nw6d+7sPsc0TZxOp0cSzGq18tJLL/Hoo49y5513EhgYyKBBg7jnnnsq5+KkSjmSmYLLNAm3BhMVpOEyBQzDIDY0ml/SjrEv7SRNw6KxlHAYivg/0+WEI3vz/7n+mFepbhNodB5GUIhvg/NnIWGk1mmBNTSIwMO7ID0pf5ji8YOYrbuWeIU0qT6Uoq1kFsPC69s+51hG2ZcvFqnuYsIi+VvHvr4OQ0TELzRq1Ihdu3ad9bgxY8YwZsyYUpcTExPDvHnzyhWjVH+mabqH3DUJi9YfjX/RIDSCvRknsTsdHMlKpVGNKF+HJBXATE+CfdvA/sfCDOHR0Kw9hu5vyYVGQtsLIPl3OPAz5Nphx+e4Uo5jGXQDRs0YX0colaTMySeHw8GxY8dIS0srsht5u3btyhVYdXYsI5XEtGRfhyEiIiIVTO0jqa5O5WaR5sjGgkHDUG+u31U1BRgWmofX5tfUY/yWdoKGoZFK0FVhZp4DDu2EYwn5G6xB0LQ91Gqo+1oGhmFArQaYNWP+7EV2eBeuV+/H6HkZRtfBGrp4Dij1HU5LS2POnDmsXbsWh8NRaH9B97mdO3dWSIAiIiIi/k7tI6nuEtKTAIixhROkPxKL1DQ8mr1pJ0h35HA8O4MYW7ivQ5IyME8dhf3bITc7f0OdJtC0LUZg8Ys3SMkYlgBoFIfZpA0k7oYDv2B+uRrz1635vaAatPB1iOJFpf7kmDVrFp988glDhw6lY8eOhIfrl6qIiIic29Q+kurMZbpISM/vtd9Qw42KZbUE0CQsmn3pJ/kt7YSST1WMmefIH2J3/ED+huBQaN4RI7KObwOrhgxbOMboOzF/3Yr56ZuQlIjrP49jnH8JRu/LMQKtvg5RvKDUyacvv/yS66+/XhNRioiIiPxB7SOpznalHMfudGC1BFDHFubrcPxabHgt9qcnkZyTRXJOFtFaWr5KMH/fB9+sB3t6/oZ6zaFJm/yeOuIVhmFgtOmJ2awd5uYVmL98hfndJsyEn7BcOgEjpqmvQ5QKVuqlpKKiomjaVC8EERERkQJqH0l19s2JBADqh0YQoJVoz8gWaKVRjfw5sfalnfRxNHI2punC3P0/XP95PD/xFBQCbXphNGuvxFMlMWzhWC4dj2XkNAiNgKQjuN78J64ta/NXGpRqo9SfHldddRXr16/H5XJ5Ix4RERGRKkftI6muck0X3588CEDD0CjfBlNFNA+vDcBRexpZebk+jkaKY+Zmwy9fwe5vwXRBTDPocJGG2fmI0aITlhsehlbng8uJ+dW7uP7zOGbKCV+HJhWk1MPubrvtNnJzc7niiisYOXIkMTExBAQUzgpfcsklFRKgiIiIiL9T+0iqq59yMsl25hEaGKQhZCUUHhRCnZAwTmRnsD89iXY16/s6JPkLM+U47P0e8nIh0Iox6Mb8CcYzU30d2jnNsIVjGX5r/lxQH78OR/fjeu0hjEHjsMR193V4Uk6lTj4dO3aMrVu3snPnzmJXbNFqLiIiInIuUftIqqtvstOA/LmMtMR8ycWG1+JEdgaHMk7ROrIuVg3h8gumacLhXfkrrUH+MK/uw7C06Ylr26c+jU3yueeCatgK14ZFcGQv5vqFuA78gtH/WgxrsK9DlDIqdfLpnnvu4eeff+aWW26hQ4cOWs1FREREznlqH0l1lGGa/JyTBUCzsFo+jqZqqRMSRlhgMBl5ORzKOEXziNq+DumcZzpyYO93kPrHXFx1m0Kz9hhhUT6NS4pmRNTCctXfMb9eg7l1PeaOzzF//w3LsFswajfydXhSBqVOPn333XdMnDiR6dOneyMeERERkSpH7SOpjr4383ACjWpEERVsIzXX7uuQqgzDMIiNqMVPyUfYn56knmM+Zmamwu7/QU4WWAIgtgNGnca+DkvOwrAEYPS+HLPxebjeW5w/Gfkb/8QYfJOG4VVBpZ5wvHbt2kRGRnojFhEREZEqSe0jqY6+MfMA6FE31seRVE2NQqOwWgKwOx0ctaf5Opxzlpl0BH7+Ij/xFBwK7fsq8VTFGE3aYLn+IWjSFvJy84fhffofTGeer0OTUih18ummm25i5cqVZGZmeiMeERERkSpH7SOpbpJMF7/hwgC61Wnq63CqpACLhaZh0QDsS0/ycTTnHtM0MQ/9Cnu+BZcTImtD/IUYoRG+Dk3KwAgNxzL6DozuQwEwv/8A16qn83u1SZVQ6mF3ubm5BAYGcskllzBkyBDq1atXaDUXwzC48cYbKypGEREREb+m9pFUNwW9nloH2aipVe7KrFl4NL+lneRUThYpOVlE6bmsHC4n7P4BTh3Nf1yvOTRti2GUuu+F+BHDYsHocwVmTCyuTUvg8C5crz2MZcQUjAYtfB2enEWpk09z5sxx//+1114r8piyNq42b97M4sWL2bt3LxkZGcTExHDxxRczdepUj4k7P/74Y5599ln2799PgwYNmDRpEldccUWp6xMRERGpCN5sH4lUNtM03cmn7iHqJVIeIQFWGoRGkpiVwr70JLoo+eR1FqeDgF1bISsVDAs074BRp4mvw5IKZLTqgqVWfVxrnofk33GtmIPR/zqMDv00t5ofK3Xy6aOPPvJGHACkpKTQoUMHrr/+eqKiotizZw/z5s1jz549LF26FIBvv/2WqVOncuWVV3LPPfewZcsW7r33XmrUqMGll17qtdhEREREiuPN9pFIZTuMi6OYBAKdg8N8HU6V1zyiFolZKfyelYo9LwZbYJCvQ6q+7OnU+n0nhjMXAoMgrhtGuFZqrI6M6PpYrrsX16aXYc93mB8th2MJMHAsRkCp0xxSCUp9Vxo2bOiNOAAYOXKkx+MePXoQFBTEfffdx7Fjx4iJiWHBggV06NCBhx9+GICePXty6NAh5s6dq+STiIiI+IQ320cila2g11MHArBZAs5ytJxNZJCN6OBQknOySEhPpk3Ner4OqVoyU08QsPt/GM48zOBQjDY9MUKUPK3OjCAbluG3Yn67CfOLlZg7PsdMOZ4/DM+me+9v/H7Qa1RUFAAOh4Pc3Fy2bt1aKMk0dOhQfvvtNw4fPuyDCEVEREREqgeXafI/0wlAd4t6D1SU5uG1ATiYkUyey+njaKof88Qh+HULhjOP3OAwnOf1UuLpHGEYBpZul2IZNQOCQvLngXrzn5jJR30dmvxFqT9RBgwYcNZxlIZh8OGHH5Y5KKfTSV5eHnv37uX5559nwIABNGrUiL179+JwOGjevLnH8S1a5E8utm/fPho1alTmekVERETKojLaRyKVYTcuUjEJBdqhXk8VJcYWTmhgEFl5uRzOTKGZhoJVGPP3fXBgBwCumvVJDq9PbQ1tPOcYsfFYrpmN6915kHIc15v/xDL8VoymbX0dmvyh1Mmn7t27F2pcOZ1Ojhw5wvfff0+rVq1o27Z8N7h///4cO3YMgL59+/LUU08BkJqav4xiRITnxIcFjwv2l4VpmmRlZZX5/JIwDAObzebVOkSqE7vdjmmavg6jXPS+Fyk9b7/3TdOs8AlJK6N9JFIZCobcnW8EEqiJeyuMYRjEhtfi51O/sz89iaZh0ZoYuZxM04TDuyBxd/6Ges1xNWgNSSd9G5j4jFG7Uf48UGuehyN7ca1+BmPA37B0vMjXoQllSD7961//Knbfr7/+yvjx4xkxYkS5glq0aBF2u529e/eyYMECJk+ezMsvv1yuMs/G4XCwc+dOr9Zhs9nU8BQphf3792O3230dRrnofS9SepXx3g8KqthvxSujfSTibbmmyQ9/JJ+6GRpyV9Ea14hiV8oxMvNyOW5PJyZUKwmWlWmasH87HD+Qv6HxedCgFeTl+TYw8TkjNALLlTMxP3gFc+cWzI+W40r+HaPf1RgWv591qFqr0E+V8847j6uvvponn3yS1atXl6scgM6dOxMfH8/IkSP54IMPaNmyJQDp6ekex6elpQEQGRlZ5jqtVqu7fG/RtxsipRMbG1stej6JSOl4+72/d+9er5VdlIpqH4l42084yQaiMWjh/1PDVjmBlgCahEWzL/0k+9OTlHwqI9Plgr3fQ/KR/A2xHTBimvk0JvEvRqAVLp0A0Q0wv1yN+cOH+RORD5+MYQ32dXjnrAr/SqNWrVoV2qiLi4vDarVy8OBBBgwYgNVqZd++ffTt29d9zL59+wAKzQVVGoZhEBoaWu54RaTiaLiayLnJ2+99XySFK7p9JOIN37j+7PVk0ZcnXtEsPD/5dDInk7TcbCKCQnwdUpViOvNg9/8g9QQYBrQ8H6NWA1+HJX7IMAyMHsMwa8bgeu8l2L8d14p/Y7l8OoYSvz5RoV9pnDp1ilWrVlGvXsUtH7pt2zYcDgeNGjUiKCiIHj16sGnTJo9jNmzYQIsWLTTZuIiIiPgdb7SPRCpahmnyM3+scqchd14TGhhEPVv+H74JGUk+jqZqMfNyYefX+YknSwDE9VDiSc7KaN0Vy5iZEBIGx/bjevNxzFPHfB3WOanUnyzjxo0rcnt6ejr79u3D4XDwxBNPlCmYqVOn0r59e+Li4ggJCeHXX39lyZIlxMXFcfHFFwNw6623Mm7cOB588EGGDBnC1q1bWbduHc8880yZ6hQREREpL2+2j0Qqw/dmHk6gERYaGBpy502x4bU4ak/jcGYK50XGEBSgZN/ZmI6c/MRTVhoEWuG8nhhhNX0dllQRRoOWWK6djWv1M5B6HNd/HsMyagZG/bKPnJLSK/VvuqLmYDAMg0aNGtGrVy+uuOIKWrRoUaZgOnTowIYNG1i0aBGmadKwYUPGjBnD+PHj3RODdu3alXnz5vHss8+ycuVKGjRowKOPPsqQIUPKVKeIiIhIeXmzfSRSGQpWuVOvJ++LDg4lwhpCmiObg5mnaBlRx9ch+TXTkQO/fAX2dLAGQ5sLMELDfR2WVDFGzXpYrrkH17tz4VgCrrf/jWXYLRgtOvk6tHNGqT9dli9f7o04AJg0aRKTJk0663EDBw5k4MCBXotDREREpDS82T4qieuvv55vvvmmyH1PP/00w4YNK3LfgAEDSExMLLR9+/btBAdrUtZzRZLp4jdcGEA3I8DX4VR7hmEQG16LbcmJJKQn0zy8tubYKoaZmw07vwJ7BlhDoO0FGLYwX4clVZRRIxLLmP/Dtf5F2P8TrjXzMQaMxdLxIl+Hdk4oUfLp559/LlWhAQEB1KhRg4YNG2LRcoYiIiJSDflT++iBBx4gIyPDY9uyZct4//336dWr1xnPHTx4MDfffLPHtoIe53JuKOj11BoLURpyVyka1IhkZ8pRsp0OjtrTaBBa9lW7qysz157f4yk7E4L+SDyFKPEk5WMEhWAZOQ3zw+WYOz7H/Gg5roxTGBeM0irVXlai5NMVV1xRphths9kYMWIEs2fPJiREKzmIiIhI9eFP7aOWLVsW2nbXXXfRu3dvoqOjz3hu7dq16dSpU4XEIVWPaZoacucDAYaFpmHR7Ek7QUJ6kpJPf2Hm2OGXLyEnC4JsfySeavg6LKkmDEsADLoBImphfvUu5tZ1+cM6B4zFUOcZrynRJ8zjjz9eqkJN0yQzM5Pt27ezYsUKTNPk4YcfLlOAIiIiIv7In9tH33//PYcPH+b222/3SvlSfRzGxVFMAoHOSj5VqqZh0exNO0FyThapuXYig2y+DskvmDlZ+T2ecrIgODR/jqeQUF+HJdWMYRgYPUfgCo3I7wW1fTNkZ8GQCRhaBMArSvSsXn755WWuICIigg0bNij5JCIiItWKP7eP1q1bR2hoaInmyFy7di0rVqzAarXStWtXZs6cSVxcnFfiEv9T0OupAwHYNOSkUoUEWqkfGsmRrFT2pyfRqVYjX4fkc2Z2Zn7iKdeen3hq2xsjWEk58R5Lh36YITVwbViEuft/mDlZWEZMwQjSyK2K5vWUXo8ePfj000+9XY2IiIhIleHN9lFeXh7vvfceAwYMIDT0zL0FBgwYQIcOHWjQoAGHDh3ixRdf5LrrruPdd9+lcePGZY7BNE2ysrLKfH51ZBgGNpuNvLw8TIejzOXk5Tk8fpaHC5NvjDwwoIvLwOEqXKYlz4mF/NeVoxxxV7Y8Z35SzdtxOxx5Hj9Lq7EtP/mUmJlKyxq1CA4IJM+S/yea3W4vciVNf1Xu13iunYBdWzFy7ZjBNXC27g6WQChFWWV5fxS8xp3OPFxV6DUO/h37me6FkZdHEH70Gm/UFmPIZKybFmMc+Jm8Ff/GMXQyVKOhnna73eNnRTJNs0TTEHg9+XTJJZdwySWXeLsaERERkSrDm+2jL7/8kuTkZIYPH37WY//xj3+4/9+1a1d69+7NkCFDWLJkCQ8++GCZY3A4HOzcubPM51dHNpuNtm3bkpJyCkfKyXKXl5KSUu4y9lkDSKsZRojLRczJJIqKymaLJBpIT0/nROqpctdZWSLNQKhXeXGX536EEkgWeew6eYT6llDMGuHQGPbv3++VPxS9pTyvcUteLtHHdmHk5ZAXGExy7Ra40tKB9DLFUpr7UfAaT0tLJzvpRJnq85WqEHtR98KaZ1IX/3uNh8aPoPlP6wg8noBrxRz2dRiBI7h6TXKfkJDglXJLslCJBjOKiIiIVCPr1q0jKiqKPn36lPrcunXrcv7555d6Jb+/slqtRU6Cfi4r+FY4KqomZmDZh7fl5TlISUkhKiqKwEBruWLaZOQBLs43AompXafIYyxhEQCEh4fjDAooV32VKTwsPP+nl+N2OPLc98NqLdufVnn2ILan/E4SubSv3Ziaf8xvFBsb6x+9QkqozK9xRw4Bu7di5OVgBtkgrgfRZZz/qizvj4LXeEREOGGGs0z1+oo/x36me2GERQH++Bpvg6tVHOb6F7BlnqLtjnU4ht+GGVXX14GVm91uJyEhgWbNmmGzVexQ1r1795boOCWfRERERKqJ7OxsPvzwQy677DKs1vIlJsrDMIyzDvk7VwUGBkIF3JvAQGu57nGuabLNlQtAj4AgrEYxCZrAgD/qC8Tq8t1rqrQC/5gwuLLitloDy3w/GgVGsyv9BDnOPE44sqgblr/yXUX/gVhZSvMaNx05sOd/kJ0JQTaMtr2xVsDk4qV6f/zxGg8ICCTAh783y6QKxF7kvQjMf3/65Ws8tAXmtbNxrXwaI+UYQf99FssVd2DUberryCqEzWar8M/nkq78q3UERURERKqJjz/+mKysLEaMGFGm848dO8Z3331HfHx8BUcm/uYnnGQD0Ri00J8EPmUxDJqFRQOwPz3Jz3qCeI+Zlws7v85f4t4aAm17aVU78QtGRG0s18yCuk3Ano5rxROYh3b5OqwqT580IiIiItXE2rVradCgAeeff36hfTfccAODBg1yP163bh133XUXa9asYcuWLbz99tuMHTuWgIAAbrrppsoMW3zgG1f+BNndjEAsWuXO55qERWPBIDXXzsnsTF+H43VmngN2boGsNLAG/5F4ql5z60jVZoRGYBnzd2gUB7nZuFY/g7lvu6/DqtKUfBIRERGpBlJTU/n8888ZOnRokV3gXS4XTuefc4I0atSI48eP89hjjzF+/Hieeuop2rVrx3/+859yrXQn/i/TNPmZ/NdCD0OzcPiD4IBAGtbIH263K/WYj6PxLtOZB79ugcwUCAyCNr0wbOG+DkukECPYhmX0HdC8IzgduNbMx9z9ra/DqrL0aSMiIiJSDURGRrJjx45i9y9fvtzjcadOnQptk3PD92YeTqARFuob+i7aXzQLr8WhzBQOZiRzKieLmsHVbwia6XLCrm8g4xQEWPMTT6ERvg5LpFhGoBXLiCmYG1/C3PU/XOtfxMi7GUvbC3wdWpWjTxsRERERkXPIVjN/yF139XryK5FBNqKDQzGBT3/f7etwKpzpcsGe7yDtJFgCoE1PjD96e4n4MyMgEGPIJIx2fcA0MTcuwbXtU1+HVeUo+SQiIiIico5IMl38hgsD6FbcCnfiM7HhtQD4/PffyHXm+TiaimOaJuz7EU4dBcMCcT0wwmr6OizxFWtwlZtY37BYMC65AToNBMD8aDmubzf5OKqqRV93iIiIiIicI775o9dTayxEacid36lni6BGYBCZeTl8c+IAfeq18HVI5WaaJiT8BCcPAwa07ooRWdvXYYkvBVoxDAPX7u/yVzusKmzhWPpfixkUjPnNBszPVuByZGP0vKzIuRbFk5JPIiIiIiLnANM03cknDbnzT4Zh0DqyLj8kHebjxF30jmle9f+oPbwLjiXk/79lZ4ya9XwajvgRezpkpvo6ilIxDAOjzxW4rCGYX67G/HoN5GbDhVdV/feql+nrDhERERGRc8BhXBzFJBDorOST32oRUYcgSwCJWSnsTj3u63DKxTyyFxL/mL+qWTxG7Ua+DUikglh6DMO46FoAzO/ex/zoNUzT5dug/JySTyIiIiIi54CCXk8djABs+obebwUHBNKzbiwAHx/Z5eNoys48fgAO/pL/oPF5GPVifRuQSAWzdLkY45IbAQNz+6eYG5fmr+goRVLySURERESkmnOZJv8z8/8o0pA7/zegYRwA25IOczI7w8fRlJ6ZdAT2bct/UL8FNGjl24BEvMTSvi/G0IlgWDB3fo1r/ULMarRYQEVS8klEREREpJrbjYtUTGoA7dAqd/6ufmgkbaPqYQKfHNnt63BKxUxKhL3f5T+o2xSatNVcOFKtWc7rgWXEFAgIhD3f4frvfExHrq/D8jtKPomIiIiIVHNb/xhy18UIJFCJgCqhoPfTl0d/I9vp8HE0JWMm7oHtm8E0IboBxHZQ4knOCUbLzlhGTYfAIEj4Cdc7z2LmZvs6LL+i5JOIiIiISDWWY5r88EfyqYeG3FUZ7Wo2oK4tHLvTwdfH9vs6nLMyjx/E9e5z4HJCVF1o2UWJJzmnGE3bYbniTggKgcO7cK16GjM7y9dh+Q0ln0REREREqrFtppMcoA4GzdX8rzIshkH/+q2B/KF3LtP0cUTFM08dxbXqacixQ2RdaNUVw6LXmpx7jIatsFw5E4JD4fffcK18EtOe7uuw/IJ+I4iIiIiIVGNbTuv1pJ4oVcsFMc0JCbByzJ7GL6d+93U4RTLTknCtfArs6VC3CXTqjxGgHnZy7jLqxWK56u9gC4fjB3CteAIzM9XXYfmckk8iIiIiItVUiuniV7TKXVUVEmild0xzAD4+ssvH0RRmZqXhWvUUpCdDzXpYRt+BERjk67BEfM6o0xjL1XdDjShIOoLrrTmY6cm+DsunlHwSEREREammvjHzMIEWWKhjqOlfFfVv0BoD+PnU7xzN8p/eE2ZOFq7Vz8CpYxAejeXKuzBCI3wdlojfMKLr5yegImpByjFcb/0LM+WEr8PyGX0CiYiIiIhUQ6Zpule566leT1VWHVs48dENgfy5n/yB6cjB9e5cOH4QQiPyE0/h0b4OS8TvGFF1sVw9C6JiIC0pPwGV7J9DaL1NyScRERERkWroMC6OYBIIdFHyqUob0CAOgK+P7ScrL9ensZjOPFxrX4DEPRBsyx9qV7OeT2MS8WdGeHR+D6haDSAzJX8I3olDvg6r0in5JCIiIiJSDRX0eupgBBCqicartPOiYmgQGkmOK48vj/7mszhMlwvzvZcgYQcEBmEZNQOjbhOfxSNSVRg1IvMnIa/bFOzp+ZOQH93v67AqlV8ln9577z1uvfVWLrzwQjp16sTIkSNZuXIl5l+WFX377bcZPHgw8fHxXHbZZXzyySc+ilhERERExP84TZNvNOSu2jAMw9376ZMju3GZrkqPwTRNzA9fxdz9P7AEYLnsNoyGrSo9DpGqyrCFY7lyJtRvATlZuFY+iZm4x9dhVRq/Sj698sor2Gw2Zs2axYIFC7jwwgu57777eP75593HrF+/nvvuu48hQ4awePFiOnXqxNSpU/nxxx99F7iIiIiIiB/ZiZN0IAxoS4Cvw5EK0KNuM2oEBpGUk8n25COVWrdpmpifr8Tc8TkYBpahEzGata/UGESqAyMkFMsVd0KjOMjNxrXqacwDv/g6rErhV1+DLFiwgOjoPyeq69WrFykpKbz88stMmTIFi8XC3LlzGTZsGLfffjsAPXv2ZPfu3Tz//PMsXrzYR5GLiIiIiPiPgiF33YxAAjTkrloICgikT72WbDr8Cx8l/kqnWo0qrW7zfxswv90IgHHxDRitu1Va3SLVjREUguXy23GtfR4SduB69zksI6ZgNO/o69C8yq96Pp2eeCrQpk0bMjIyyMrK4tChQyQkJDBkyBCPY4YOHcrXX39Nbq5vJ98TEREREfG1LNNkm+kEoIeG3FUr/Ru0xmIY7E49TkJ6UqXU6dr2CeYXqwEwLrwKS3zfSqlXpDozrEFYLpsKLbuAMw/Xmucxd3/r67C8yq+ST0X57rvviImJISwsjH379gEQGxvrcUyLFi1wOBwcOnTuzRgvIiIiInK6/5l5OID6GDTx/+a+lELN4FC612kGwPuHd3q9PtfOLZgfvQ6A0WM4lq6DvV6nyLnCCLRiGXYLRlwPcDlxrX8R1y9f+Tosr/Hrr0K+/fZbNmzYwN133w1AamoqABERER7HFTwu2F8WpmmSlZVV5vNLwjAMbDabV+sQqU7sdnuhBQeqGr3vRUrP2+990zQxNAxJqrEv/xhy19uw6rVeDV3SqA1bju/n+5OHOG5Pp64t3Cv1mL/9iLlxCWBidBqAccEor9Qjci4zAgJhyASwBmHu+Bxz41JceQ4sHfr5OrQK57fJp6NHj3LHHXfQo0cPxo0b5/X6HA4HO3d699sDm81G27ZtvVqHSHWyf/9+7Ha7r8MoF73vRUqvMt77QUFBXi1fxFcOmU4O4SIQDbmrrhrWiKJ9zQbsOHWEDxN/5bqWFT//knnoV1zrFoDpwmjTE6P/tUpkiniJYbHw/+3deXxTVfr48c/N2r2lUMpSCqVAKfsqq4ggCoqiCLjBIKCiIq78voMzjjruOjoqiwoIKioKAqOyi6IwgDKKsiNbWVso0NIlXdIk9/z+SBuJLdA9TXner1debU/uPfdJb25y8+Sc5zLoL2Ayo7atQ307H91ZgKHLIF+HVqlq5DtSVlYW9957LxEREUyfPh2DwT1cODw8HIDs7GyioqK8lj///vIwm820aNGiAlFfmrxgC1E2cXFxtWLkkxCibKr62D948GCV9S2ErxWNeuqoGQmR96Ba67qYRHadS2FzahJDY9sTZgmotL7VqSPoX04DlxPiO6FdOw5Nk+mbQlQlTTPA1XeCyYL6ZTXqh8/RHQUYetzg69AqTY1LPuXn5zNx4kSys7NZuHAhoaF/DCNt3rw5AElJSZ7fi/42m800adKk3NvVNI2goKDyBy6EqHQyXU2Iy1NVH/uSFBa1VYFS/O+8KXei9moZXp9moXU5kp3GDyn7ualZh0rpV6WloC99Exx2aNIaww33u6cFCSGqnKZpcOUIMFtRP36F2rQU3WlH631LrTh3qVEpbKfTyaOPPkpSUhLvv/8+0dHRXvc3adKEZs2asXr1aq/2lStX0qtXLxlCL4QQQojL0tKlS0lISCh2e/311y+6nlKK2bNn079/fzp06MBtt93Gtm3bqidoUem2KRd5QCQaCTXrNF9UMk3TuLZxIgDfn9xPvstR4T5Vxmn0xW9Avg2i4zAMewjNJElMIaqTpmkYet2E1m8kAGrLCtT6hX4/GwRq2Minf/7zn3z//fdMnToVm83mdfLTpk0bLBYLkydPZsqUKcTGxtKjRw9WrlzJjh07+OSTT3wXuBBCCCFEDfD+++97jRr/8xd5fzZnzhymTZvGlClTSEhI4NNPP2X8+PF89dVXFRpRLnxjk3InIHprJgy14FtycXGd68UQFRDCmXwbm08lMaBxQrn7Ulln0b/4F+RkQN1GGIY/imaREehC+Iqh22B0kwW17lPUr2vBWQADR/v1FNgalXzatGkTAK+88kqx+7777jtiYmIYOnQoeXl5zJkzh9mzZxMXF8eMGTPo3LlzdYcrhBBCCFGjtG3blsjIyFIta7fbmTVrFuPHj+fuu+8GoGvXrgwePJi5c+fy7LPPVl2gotKdUTr70dGAXlJo/LJg0AwMiklkwcGf+Tb5d65q1BJjOT6Yqux09C9eh+x0qBONYcQUtMCQKohYCFEWhk4D3AmotR+idqwHRwFcNw7NYPR1aOVSo96Z1q1bV6rlRo4cyciRI6s4GiGEEEKI2uvXX3/FZrMxZMgQT5vFYmHQoEGsXbvWh5GJ8thcWOspESORfvzNuCibXvXjWHZ0B2n2HH45c5Qe9ePKtL7KyURf/DpknoHwKAwj/h9acPkv4iSEqFyGdn3RTWbUqvdRe39EOQswXH+fX9Zik3cmIYQQQohaYujQoSQmJjJw4EBmzZqFy+W64LJJSUkAXhdxAYiPjyclJYX8/PwqjVVUHpdS/FhUaNzgfx9IRPlZjCYGNHJPt1t1fA96GerCqNxsd+LpXCqERmIYOQUttE5VhSqEKCdD6x4YbnwQjCY4sBX965koZ8XrvFU3eXcSQgghhPBzUVFRTJ48mY4dO6JpGuvWreOtt94iNTWVp59+usR1srKysFgsWK1Wr/awsDCUUmRmZhIQUL7LtyulyM3NLde6tZWmaQQGBuJ0OlGO8n9ocBZ+4HCe98FjFzqZBkWwgkSnjoPK+1BicLow4L4wkKMCcVc3p8udjKvquB0Op9fPyuAsTCDm5+eXqshwzzqxfHNiLydzM9mScohOkY0vvZH8HEzLpmNIS0EFh+O4cTKYgyEvr9xxa5pGQEBAhZ/jFVHS8XEpRc9xl8uJ7kfPcajZsV9sX9TkuC9GczqxUPpjs1I1bo02+D5Mq+egHd6Bc+mbOAffB2brpdcFCgoKMJvN5FXgGL8QpVSprsYnySchhBBCCD935ZVXcuWVV3r+7tu3L1arlY8++oj777+f+vXrV2s8DoeDvXv3Vus2a7rAwEDatGlDRsY5HBlnK9xfRkaG5/cfwoPAaqZ9np0MW2aF+z5fYGA4kUB2djZnMs9Vat9VKVyZoEH1xX3+/qgoLTQcmlDq5G8ggQxonMCKY7v4NnU/PRrFX7TgvLLnoq98F86egKAwjCP/D1Nkg8oKn8zMTArOnam0/sqjLPuj6DmelZVNfppv4y4rf4i9pH3hD3GXxGoMoK5S5f5ipsISuqKCQtC/fBvDiX1YVr2H4eZH0KyXvjhAYGAgYaGh7Nq9u0oS8haL5ZLLSPJJCCGEEKIWGjJkCPPmzWPv3r0lJp/CwsIoKCjAbrd7jX7KyspC0zTCw8tf98VsNtOiRYtyr18bFX0rHBFRB2Uq/5XonE4HGRkZREREYDKZSUNxQHN/kBgYEEK9gNBL9FA2hpAwAEJDQ3FZ/KfIbWiI+/9Q1XE7HE7P/jCbK+ejVbg1yN334Z2o9NRSrXOl0vkWjRM5Gfz6v5V0MF1gNITTgWHnD2hZZ1FmK3qbvrgO74LDuyoctxYZjTmuPRER4ehG31wW/s/HR2kUPcfDwkIJ0S48VbkmqsmxX2xf1OS4L8YQGYWmaTh/34Kek+27QNr1cx/HyQdwzX8avX3/S46A0q1BBLTrTWxsbKkSRWVx8ODBUi0nySchhBBCiMtQUa2nw4cP07p1a097UlISjRo1qtA3u5qmERQUVOEYayOTyQTm0n0ovng/ZsxmMz/qBSgFiRiIMVfuBwr3hoyF2zNh1ised3UxFRbjra64zWYT5krYr1D4HAGMDjvYc0q1TjjQXzOxRjlYU5BNZ6ej2DQY5XTA3h8hJwOMZrTWPd3bKuU2Lslhd8dtNGGspP9FeRUdH6Vb2P0crwlxl5kfxF7ivvCDuEtUGLfBnoehso6b8rAGohJ7w94f0bLTMW77FhJ7oV0kAVU0MdhqtRIYeOmRUmVRmil3IAXHhRBCCCFqpZUrV2I0GmnTpk2J93fp0oWQkBBWrVrlaXM4HHzzzTf069evusIUFVCgFJuUe9TTVQY/+gAnqsRAzYwFOIbObrxHkyhnAezd7E48mczQprdc1U4IP6YFh0Ob3u4RT7lZsHsTqqDy6zlVJhn5JIQQQgjh5yZMmECPHj1ISHBf9eq7775j0aJF/OUvfyEqKgqAsWPHkpKSwtq1awH3t58TJ05k+vTpREZG0qpVKz777DMyMjKYMGGCzx6LKL1flJMcIBKN9vjPlDhRNUI1jSs1E98pJ6t0B20NRjRNQzns7hFPuVlgsrhHSEjiSQi/pwWFodr0cR/f+TZ3AiqxN1pAzRx5LMknIYQQQgg/FxcXx5IlSzh16hS6rtOsWTP+9re/MWbMGM8yuq7jcnmPhrj33ntRSjFv3jzS09NJTExk7ty5NGnSpLofgigjheIH5d6fV2mmixaYFpePQZqZDcpJEjr70EkoKJxql5ftHiGR2BstqHLrggkhfEcLDEG17QN7NoM9F/ZsRLXuVSOPc0k+CSGEEEL4uaeeeuqSy3z88cfF2jRNY+LEiUycOLEqwhJV6AiK4+iYgd6aTLkTbuGagT6aiR+UkxWufBL2bHGPiDAHuKfaBYb4OkQhRCXTrEGotn3dU2vzbLBnE6p1D7SQOr4OzYvUfBJCCCGEEMLPbNB0ALppJkJk1JM4z7WaGZOCgxr8bjGDJRDaSuJJiNpMswRAmz4QHAHOAtizGZV5xtdheZHkkxBCCCGEEH7EZtDYhjv51F+TiQzCW4Q9l75pJwH4qkkrVJveaAGSeBKittPMVncR8rB6oLvg9y2o9JO+DstDkk9CCCGEEEL4ka0BFlwaNMdArCaFxsUfVE4m7N7E4OMHsbpcHA0KYbv1wpdfF0LULprRBK17QGRDUDrs/xl1+pivwwIk+SSEEEIIIYTfcKHYGmgBoL/UehLnUVlnYc8mcNgJswQwoHBU3Nd6AS6lfBydEKK6aAYjtOwKUbHuhqRtaMf3+jYoJPkkhBBCCCGE39iGjs1oIFRBZxn1JAqp9FOw9ydwOSG0LrTpwyBTIMHAKRRblNPXIQohqpGmGaB5R2gYD4AhaRv6L6t9GpNMEhdCCCGEEKIGU84CsOeh7DmsCwwEi4Urz53BeO4sSne5a3soBUYjGExgLLyZrRAQBNZgCAhGM8lIqdpInT4KSTsABXUaQMuuaAYjgcB1moWlqoAVykF3ZcIsxemFuGxomgZN26JMFji+F3VoG7S9ymfxSPJJCCGEEEKIGkK5nJCTAdnnwFZ4c9gB2B9ah+Otu2B2ueh/dC84HWXr22SGoHAIqQMhERBSx32FJOGXlFJw/HdIOeBuiGoCzTu6RzwUukozsU45SEexQTkZKFM1hbjsaI1b4qzfDPMVg30ahySfhBBCCCGE8CGVZ4Nzp9y37HNACfV5zBa+jXFPn+iWlUlgdByYLWAwum+aBi6Xe9qV7nT/LMiH/Byw57oTWE4HZJ1134q2bQmEiPruW1g9GR3lJ5TugkO/QVqKu6FxK4hJcI90OI9F07hBM/OpKmC1KqCPMhEgo5+EuPyERKAFhkBens9CkOSTEEIIIYQQ1UzlZcOZ45B+CvJt3ndaAt2jk0LruH8GhZFiMLBbz0NT0FU3oxrEo5lLnyhSLqc7EWXL+GNEVV42FOTB6aPum6ahQiKhTjTUbYxmDazcBy0qhXLYYd//3PtQ09yjnYoKC5egl2ZirXJwGsW3ysFQzVKN0QohhJskn4QQQgghhKgGqiAflXIAju9zJw6KaBqE1XPX64mIRgsIKrbuWt099a4jGpEuvczb1owmCA5336KbuuNxOSErDTJPQ8Zpd3IqO819O7YHFRoJsW1QjVuW7wGLSqdys2HfFvdoNqMZWnVHC6930XWMmsZNmoX3lZ21ykEfZaKOJtedEkJUL0k+CSGEEEIIUYXUuVOord+g9v7kqd8EmmeEERH1Lzrd7azS+bnwamUDVeVd4U4zmtwx1Il2x5mf405CpaUUJqHSYfdG9D2bSGwQx+GmiZxuGIcyyFX2fEGlpbin2ukusAZB6x5ogaGlWreLZiReGTiEzlJVwARNan0JIaqXJJ+EEEIIIYSoAupkEvrPq+Dgb3jqOAWGQr0YiGpS6mLf3ygHOtAGI00xcPaSa5SPFhAMDeKgQRyqIM+dhMo8AxmniTyZROTJJOzWQE40bcPxZm3JDa1TRZGI8xUrLB5Wz31FO7O11H1omsZtBgsv6/n8olz0Uy5aapJEFEJUH0k+CSGEEEIIUYnUsd/Rf/oaTuz7o7F5Rwxdr0VPO4mWm1Xqvs4pnR8LRz0NMZhBL/uUu/LQLIHQMB7a90OLasKxn74mKmkHVnsu8fu3Er9/K2eim3KkRSfORDd1Tx0UlU45C+DAVncSENz7JDbR64p2pdVEM9JHM7FROVmkF/CkIQCD7DchRDWR5JMQQgghhBCVQJ06gr5pCRzd424wGNFa90DrNhitXmMAtPRTZepzrXLgBFpioIVmxD0GqnppdaI52qk/O1t3o/7JIzQ5souoU0eISj1KVOpRbCERHI3vxImmibjMUsy6sqhzp2DnBnd9J4PRXVi8XkyF+rxJs7BVOTmBzibl5EpNrm4ohKgeknwSQgghhBCiAlT6SfRN/3GPUAF30qnDVWjdh6CFRpa730yls9Ez6sn3SR1lMJLaOJ7UxvEE2jJpmrSdJkd2E2LLoO32H2i1ezMnmrXhaHxHckMifB2u/9J19C3LYfOXoJS7vlOr7mjB4RXuOlTTGKpZ+EIV8JUqoIsyESyjn4QQ1UCST0IIIYQQQpSDys9Bbf4Ktf17UDqgoSX2ROs9DC08qsL9r1IOHEBzDLSmZl2dLC8knN879ONAm540PrqXZge3E2I7R9zBbTQ7uI3URvEcbtmZc3UbyZS8MgjIzabd1v+gTh9zN9RtDHEdLlqQvqyu0kxsVA5OolihChillb52lBBClJckn4QQQgghhCgDpeuoXf9FbVwK+TZ3Y/OOGPoOr/C0qCJp5416uslgQauhCRyXycKx+I4ca96BeqnHaHbwN+qnHqVByiEapBwio059DrfswqnGLeQqeRejFA2SD9Dut++xFOSD2QptekNASKXve6OmMdJgZZqez3rlpI8y07gcNaSEEKIsJPkkhBBCCCFEKamUg+jrFsDpo+6Guo0w9L8DrWmbSt3OSuXABSRgIMEfrkqmaZxt0JSzDZoSnJVO3MHfaHx0LxHnTtP5f6vJCwzhSHwnjse1w2mRkTbns+bZaPfb90SfTALAFtmAsGEPo84ch7PJVbLNRM1IJ4xsw8UC3c4TUnxcCFHFJPkkhBBCCCHEJaj8XNTGJagdP7gbrIFovYahdbwazVi5p9Snlc5P54168jc5YZHs6jKQfW17E5u0k2aHthOYZyNx10Za7t3C8WZtOdKiE3khFa9h5NeUosnhXbTeuRGzswBdM3CodXfOtO/HlXWi3cmnKjTSYGGvnkcSOv9VTq6S4uNCiCokySchhBBCCCEuQCkFB7aif78AcjIB0Nr2QbtyBFpQWJVs80u9AB1oh5Hm/jDq6QIc1kAOJV7B4VZdaHR8H3EHfiM0K424Q9todmg7pxq760JlRDa87OpChWacoc329dQtHNl0LrIBO7sMxBZej3Bj9ezzSM3AzZqFhaqAL1UBHZWRCJl+J4SoIpJ8EkIIIYQQogQqOx193adwaJu7ISIawzV/QYttXWXbPKhc/IYLDbjFD0c9lUQ3mjjRrC0nmrah3uljNDvgrgvVMPkgDZMPci6yAYdbdia1UQuUoXYnP6x5Nlrt+ZGYI3vQAKfRxP62vTnSoiP4IPHTTzOxRTk5gs4CvYAHDNYaW19MCOHfalTy6ejRo8ydO5ft27dz4MABmjdvzvLly4st98UXX/D++++TkpJCXFwcjz32GFdffbUPIhZCCCGEELWNUgr1+xbUuk/BngsGI1r369F63FCpVx0rabtL9AIA+mgmGtW2USiaxtnoppyNbkpIVhpxB36j0bHfqZN+ijpbVpEXFMqxuHYcb9aWgoBgX0dbqYxOB3EHfqX5vl8wudxTKlNiWrGvXR/ygqtmBF1pGDSN0QYrL+t57MTFFuWkp0y/E0JUgRqVfDpw4ADr16+nY8eO6LruHub8JytWrOAf//gH999/Pz179mTlypU89NBDfPrpp3Tq1Kn6gxZCCCGEELWGyrOhvvsEtf9nd0N0HIbrxqHVa1zl296qXBxBxwoMreUJAFtYXXZ2vYZ9bXvRNGknsYd2EJibTcLuH2m5ZwupjZpzrHl70qKa+PWUPFOBnaZJ22l2cBtWex7gnmK3t0M/Muo29HF0bo01A0M1M18pB4tUAQnKSJ3alvgUQvhcjUo+DRgwgGuuuQaAqVOnsmvXrmLLTJs2jRtuuIFHH30UgJ49e7J//35mzpzJnDlzqjNcIYQQQghRi6jDO9G/+cBd20kzoPW80T3ayVD1NXjsSrFUuUc9DdLMhF8mH/4LAoI50KYnhxK60eDEAWIP7yQy7aRnSl5OcDgpsQkkxyaSGxLh63BLzZKfQ9yB34hN2onZ6d6vuUFh/N6+D6cat6xxCbVBmpnthcnPj/UCHjJY5ep3QohKVaOST4ZLzPE+fvw4R44c4f/9v//n1X799dfz2muvUVBQgMVSO+bGCyGEEEKI6qEcdtT6RX9cyS6yIYbB96A1aFZtMaxSDs6hiERjUC0f9VQS3WgipWkiKU0TCc08Q2zSLhod+53gnExa7v0fLff+j3ORDUiObc2pxi1q5rQ8pah75jgxR/bQIPkgRt0FQHZYXQ4ldONkTEtUNSQyy8OoaYw1WHlJz2MvLr5XTgZehs9DIUTVqVHJp0tJSkoCIC4uzqs9Pj4eh8PB8ePHiY+P90VoQgghhBA+s2rVKr7++mt2795NVlYWTZs2ZcyYMdx6660XLR48YMAAkpOTi7Xv2LEDq9ValSHXGCrlIPrquZBxGgCt8zVofW9FM1ffF5qnlM63ygHAKIMFy2U+4iQ7PIrdna/m9/Z9iU45RKNjvxOVesxdGyr9FG23/UBGZENSGzUntWFzcsIifRpvWF42rX9PIvbEPgJzsz3t5yIbciihG6cbxtW4kU4laaAZuFWz8Hnh1e9aKQNN/Phqi0KImsWvkk+Zme7L24aFeRflK/q76P7yUEqRm5tb/uBKQdM0AgMDq3QbQtQmeXl5JdZ+8ydy3AtRdlV97Culat3VnD788EMaN27M1KlTqVOnDps3b+Yf//gHp06d4qGHHrroutdddx3jx4/3arscRpIrlxP149eon1eCUhBSB8Pg8Wixbao3DqVYpBfgAtphpAPyYb+Iy2QmJbY1KbGtsebl0Oj4Phqe2EfEudPUST9JnfSTtN61iZzgcNKiYkiPiiG9XiPyg6q2gLfB5STyzAmiUo8SdfIIITkZnvscZispTVpxomkbMutE+0XS6Xz9NBN7lYvtuJir23nSEIjVzx6DEKJm8qvkU1VyOBzs3bu3SrcRGBhImzbVe0IjhD87fPgweXl5vg6jQuS4F6LsquPYr23JlXfffZfIyD9Gf/Tq1YuMjAw++OADHnzwwYuWNqhXr95ld9EWdTYZffX7cPoYAFpiT7Sr70ILCKr2WP6nnOzFhQkYabDUusRoZbEHBnO4VRcOt+pCQG429U8eJjrlEHXPnCA4J5PgnExij+wG3LWVCqKaoDduSaRBwxkUSk5IOJSjjpamuwjJSics4wxhmWcIyzhDRPopz5Q6AB2Ns1ExJMe1JbVRPLrRfz9iaYVXvzui55GKYoGyczdWeV4KISrMr14Zw8PDAcjOziYqKsrTnpWV5XV/eZjNZlq0aFGxAC9BXrSFKJu4uLhaMfJJCFE2VX3sHzx4sMr69pXzE09FEhMTWbRoEbm5uYSEhPggqppHKR21dS1q01JwOSEgBMM1Y9BadfNJPFlK8UVhkfHrNTP1L5Mi4xWVHxTKsfgOHIvvgMlhJ/JMMpFn3bewjNME5WYRdHQ36uhuEoFEwGUwUhAQhN0aRIE1ELs1CKfZijrvbVpTYHbkY8nPxWrPxZqfi8Wei6GE16O8wBDORDflVL0Y9mqBRDRshNlcO2okhWgaEwxW3tLz+Z9yEYeT/lL/SQhRQX6VfGrevDngrv1U9HvR32azmSZNmpS7b03TCAqq/m+7hBAXJtPVhLg8VfWxf7kkhbdu3Up0dPQlE0/Lli1j0aJFmM1munXrxpQpU0hISKimKKuPyjqLvnoenNjnbohrj2HQ3Wg+vILaImUnB4jBwLXy4b5cnGYrpxs153Qj92cDo6OAOuknicm10Tg/l+xTSQRmnsXochKYm+1Vk6m0CsxWsiKiyA6PIisiisw69bGFRoKm4XA4cJw5W9kPy+daakZu0SwsUQUsVgXEKgPNpf6TEKIC/Cr51KRJE5o1a8bq1au55pprPO0rV66kV69etW4IvRBCCCFEefzyyy+sXLmSv/71rxddbsCAAXTo0IFGjRpx/Phx3nvvPe68806+/PLLCn2pVx21NEtNKQz7tmDatATNkY8yWXD2Ho6e2Ntdj6ea4iyqAeh0OlEOB7+hs9XgwqDgDmVA153opejH6XR4/axqBqcLA+B0OnE4qmebFeFA42RkI4yxdWjSoDlbj+8lI99GYJ6NAHseVnsuFnseAfZcTCX8Dx0mC/aAIOzWQPKtQdgDgsm3BhWv3eR0upd3eP+sDE6DOxHpcrrQffg/74fikKaxTVPMcuXzhDJThwsn74ueKy6X02dxl+f4qAlxl1dNjv1i+6Imx30x/ho3gG5yv0bZ7fZKH11e2lqaNSr5lJeXx/r16wFITk7GZrOxevVqAK644goiIyOZPHkyU6ZMITY2lh49erBy5Up27NjBJ5984svQhRBCCCFqhFOnTvHYY4/Ro0cP/vKXv1x02aeeesrze7du3ejTpw9Dhgxh7ty5PPvss+WOoTpqaZaGqSCXmP3riUg7DIAtrAHHWg+kQAuH33+v1liKagBmZJwjLSuNzyJDAAO9c/MJzLFT1rEzGRkZVRBlcYGB4UTiLntxJvNctWyzMoQrEzT4c9xGMIZCUCiUZsKDA3DkQfala9BV5v4whtcBINuWRd7ZM5XWb3lcp0FKnRBOm4y848xnXIYNywU+txY9V7KysslP823cZdkfNSnusvKH2EvaF/4Qd0n8NW4Ac4QiEEhJSamSupqlGQhUo5JPaWlpPPLII15tRX/Pnz+fHj16MHToUPLy8pgzZw6zZ88mLi6OGTNm0LlzZ1+ELIQQQghRY2RlZXHvvfcSERHB9OnTL1povCT169ena9eu7N69u0JxVEctzUsxHNmJ6X9L0PKyUQYjru7XY+54DfFl/J9UlqJvhcPCI5hvdpKvKWKVxq2BoRgDS391NqfTQUZGBhEREZhMVT9VzxDiji00NBSXxX+mXYWGhLp/VnHcDofTsz/M5sr5aBVqdWfGQkPCCK4XdYmlq94DKP6tHKSajSyvF8E9yoShhBFQRc+VsLBQQjRXsfurQ3mOj5oQd3nV5Ngvti9qctwX469xA+iB7in4jRo1qvQZY6WtpVmjkk8xMTHs27fvksuNHDmSkSNHVkNEQgghhBD+IT8/n4kTJ5Kdnc3ChQsJDQ31WSy+rKWp7HmoHz5H7d7obqjbGOOQezDVj/VJPH/2vZ7PAU1hASYYAwgoZ5Fxk8lcPQWuTcbC7Zkw6/5Tl8pUeMW56orbbDZV2v4wmdyxG01GjDWgiHkD4H5l5E09n12aYolBcYdmLj7NpvC5YjSafB53mY6PGhR3mflB7CXuCz+Iu0T+GjfgLHxNtFqtlV5bs7S1NGtU8kkIIYQQQpSd0+nk0UcfJSkpiU8//ZTo6Ohy9ZOamsrWrVsZNmxYJUdYPdSJfe6i4llnAQ2t67VofW5Bq4YRQqWxLyOVr+zugtejNItc3U74jeaakXEGK+/rdv6rnIShMVSTertCiNKT5JMQQgghhJ/75z//yffff8/UqVOx2Wxs27bNc1+bNm2wWCyMHTuWlJQU1q5dC8Dy5cv5/vvvueqqq6hfvz7Hjx9n9uzZGI1Gxo0b56NHUj7K6UBtWorauhZQEFYPw+DxaDE156p95+y5zPl9EwrooZnorclpuPAvXTQTt2uKz1QBK5SDQF1joKFmJHaFEDWfvOsJIYQQQvi5TZs2AfDKK68Uu++7774jJiYGXddxuf6oURETE8Pp06d56aWXyM7OJjQ0lJ49e/Lwww9X6Ep31U2dOoK++n1IPwmA1rYvWv/b0ayVO62gIhy6i9l7N5LtyCfGYOJOLKWepiBETdLPYCZbVyxXDharAjQdBkgCSghRCpJ8EkIIIYTwc+vWrbvkMh9//LHX3506dSrW5k+Uy4n6aTnqfytA6RAcjmHQWLTmHX0dmhddKT7a/xNJ2WcJMpm5L6AOlrwcX4clRLldr5lxAquVgy9UAUgCSghRCpJ8EkIIIYQQfkWdOYG+Zi6cPgaAltAdbcBotMKr+dQkXx/dwc9njmLQNCYmXknU0d99HZIQFaJpGjdhRgFrChNQObpiqFK+Dk0IUYNJ8kkIIYQQQvgFpeuoX1ajfvwKXE4ICEYbOBpDwhW+Dq1EP6TsZ9Xx3QCMbnEFrSMaoEvySdQCmqYxDDMWYJlysFI5sGWf5g6l+zo0IUQNJcknIYQQQgjhF9TODaiNS9x/NO/onmYXHO7boC7gx9QkPjv0CwA3NGlHnwbxPo5IiMqlaRrXaxaCdY2FqoANeZmk797AeKVTcyquCSFqCkk+CSGEEEIIv6BFNUE1bukuKt62T40t2v3z6SN8tH8LAAMaJXBj0/Y+jkiIqnOVwUyo0vhQL2DXuRReN5i4HzNRmsHXoQkhahBJPgkhhBBCCL+gNYrHeNtUX4dxURtPHeSTA/9DAX0bxDOqeZcamyQTorJ00UxERkbzbvZpUhz5vIyTsQYrHTX5uCmEcJN0tBBCCCGEEBWklGLtib18XJh4urJBC+5q0V0ST+Ky0cwcwN86D6a50Uwe8J5uZ7FuxyGFyIUQSPJJCCGEEEKICnHpOgsO/sziw78BcG1MIne16I5Bph2Jy0wdaxCPBdXj6sIRT98pJ6/qeZyQQuRCXPbkHVEIIYQQQohysjnymbb7ezacOogG3BrXmeHNOsmIJ3HZMmkaowxW7jdYCQGSUbyi5/G1XiCjoIS4jMkkXCGEEEIIIcrhYOZp5vy+iYyCPKwGExNa96Zj3RhfhyVEjdBRMxFnMLJAt7MdF6uUg63KyXCDhQ4YJUErxGVGkk9CCCGEEEKUgVN3ser4blYe242OIjowjPsS+xATXMfXoQlRo4RpGhMNVrbhYqFewGkU7+l2WmBguMFCnGb0dYhCiGoiySchhBBCCCFK6Uh2GvP3byE5NwOAnvWbcUeL7gQYzb4NTIgaStM0OmOitcHIGuVgnXJwEJ3X9Hy6YOQmg4VoqY8mRK0nySchhBBCCCEuIasgj6+O7mDTqUMoINRs5Y747nSp10SmDwlRCoGaxs2ahauUieXKwY/Kya+4+E3PoyNGBhrMxGOQ40mIWkqST0IIIYQQQlxArrOA75J/59vk38l3OQHoUb8ZI+O6EGoJ8HF0QvifOpqBMZqVAcrMV3oBO3GxDRfbdBfNMHCNZqaTZsQoSSghahVJPgkhhBBCCPEnmQV5fJ+yn/Un95PrdAAQGxLJ7fFdiQ+L8nF0Qvi/xpqBB40BnFQ63ykHW5STI+i8r+yEKY3umpGempkYmZInRK0gySchhBBCCCEAXSkOZJ5m46mDbD17HJfSAWgUFM6NTdvTqW4TDDIaQ4hK1VAzMFqzcpOysF452KAcZKH4Tjn5TjmJwUBPzURnzUikJKKE8FuSfBJCCCGEEJctXekkZaWxLe04v6Ud52x+jue+FmFRDGzcmk51Yyov6RQYUqHVNacTs1OhhUSAqRpO5a1BVb8NUTJrEASH+zqK0it6rpTzOR4G3AhcrxS7nHZ+cuSy05nPCXQWqwIWK2hiMNPJHEBHUwCNDKZi9aHKdXzIc1yUVQVfx31BMwf6OgRJPgkhhBBCCP+hlKpwQWKbw87BzNPsPJfC9rRksh35nvsCjGauiGpKnwbxNAutW9FwvSilMLTqVqE+LED9yglH1FBWg8n9XGnaBpq28XU4ZVIZz3ED0LnwZnPY+fnMUX45c5RDWWc4rjs4bnewzJ5NXWswiXUakBjRgNYR0YSYA8p9fCilwGytUNziMmCyVMpz3BcMVM77Z0VI8kkIIYQQQvgNTdP49ewxbA57qZZXSmFz2knLz+F0vo3TedlkFuR5LWM2GGkcFE6TkDo0CgrHZDByzJbOMVt6pcUdFRBCYp2GbD28HVvG6XL341I6+fn5BAQEYKyGKUhR9ZvSpnErVPY51LlTVb69yqLqN4WoWF+HUS5mgxFN0yr8XKluRc+Vyo7bAFwBdAiqS7LLznFnAadcBaTZc9h46hAbTx0CoI7BRLTRRB0XNAoIwmowlqr/kIj6dI3riDKZKy1mUUsZ3aPtth7cii278t4fqkNwSB26tfRt0kyST0IIIYQQwq/YHPZiCSSlFAW6ixyHHZvTTlZBPlmOfLIK8nEW1m46X4jJSt2AYBoEhlI3IBhDYSInx1lQJTGHmNyjKmz5OWTmZJS7H5fuIjc3lyBnEMZSfriuiJCCwnEkLieUMuFXIzidvo6gwir6XKluRc+Vqoy7XuHNiYl0FGdRnEUnGzinOzmnu/e7lpNPmKYRiUadwlvAhUZ8BARXSayi9rLl2fzq2ATQjb5P/fg+AiGEEEIIIUrJ7nKSbs8lNTcLm9NOjsNOjrOAHIcdRwlJJgADGqGWACKtQYW3YKw14ERcCFE+Jk2jPlrhFDsj+UqRhuKMcnFWKewGjUwUmSgOF64TqPAkoupgIAx8OgVJiMuNvOsKIYQQQgi/sD3tBLP3bixxJFORQKOZYLOFUHMA4ZYAwsyBhJitcpU6IWqxAE2jMRoNlCInLxdDUCCZmoFzKM6hkwXkAXkoUlCAjhEIVxr1bekEnT5CrLOAej6uiSNEbSbJJyGEEEII4Recuo7CXZA5yGQh2GwhxGQl2Gwh2GQl2GTBaJBLsQtxOdOAQDRCNAONATDiVIoMVGEyyn1zAuko0vOz+H3fZgCCgKYYaKoZaaoZaIaBcDRJSAlRCST5JIQQQggh/ELXqFg61Yth06lDxWo+CSHEhZg0jXpo1Cv8WymFDchAkRcQhMNs4UR2Orko9qKzV+mg3MuGodEUA800A001A7EYCZVklKhGulIUAPko8oECwIHCAYW3P353odABHQrH+BX+nnmSs0m/cmMj311BU5JPQgghhBDCb1THFd6EELWbpmmEAqFohIfU5arE3hTs+5nks8c5qnSOonNU6aSgk4ViJy52KpcnIRWBRgwGYjQDTTQDMRiohybTe0W5uJQiF8hFkYsihz8STfkoKutSD77+0kaST0IIIYQQQgghLmsmTSucbvfHVSQLlOI4OkeUzlFcHFU6p3FP4cvAxa7zElJW8CSkYnAnpRpiwCIJKYH7ueROLkEOf/yeW5hkuhQN93MsAA0LYAbMaIU///jdVLisAffFNgyFv4eGRnJ9Qm/sdt9dtdQvk0+HDh3ihRde4LfffiM4OJhhw4bx6KOPYrFYfB2aEEIIIYRPlPf8SCnFnDlzWLBgAenp6SQmJvLkk0/SqVOn6glcCCFqKIumEY+ReM2I+yM+5ClFCjrHlc4JdE4onWR07MAhdA4VXRBBuZMA9dFogIGGmoEGGGiguf+2SlKqVlHKnUS6UILJcYn1TbhrjgWhEYRGIO5EU0DhTysVuzpjkMHo89plfpd8yszMZOzYsTRr1ozp06eTmprKK6+8Qn5+Pk8//bSvwxNCCCGEqHYVOT+aM2cO06ZNY8qUKSQkJPDpp58yfvx4vvrqK5o0aVJNj0AIIfxDoFdCys2lFKkoTiid4+icUC5OoGMDUlGk4mK7crkXLhwpFelJSmlEYyBKMxCFRh2ZvldjFeg6aTkZnHDkcUa5vKbK5eGurXQxVoqSSxBcmGQqSjhZqFhyyR/4XfLp888/JycnhxkzZhAREQGAy+Xin//8JxMnTiQ6Otq3AQohhBBCVLPynh/Z7XZmzZrF+PHjufvuuwHo2rUrgwcPZu7cuTz77LPV8wCEEMKPGTWNRmg00gxcUdimlCITxUkUJ5XOKXROKZ2ThUmpdBTpuNijilYo7At3YioKA/U0jbpo1MFAHc2dmApHw1TLkxS+4CrcX0VXRcxQinPont/Posg6sQNO7LhgH+4rLf4xein4vN+D4LLfb36XfNqwYQO9evXynFgBDBkyhGeeeYZNmzYxfPhw3wUnhBBCCOED5T0/+vXXX7HZbAwZMsTTZrFYGDRoEGvXrq3qsIUQotbSNI0INCKAxPNGSQHYlOLkecmo00pxBp00FC7gDIoz/FFPCvD8ruEulF4HjfCMZEL3uwjJzyJEdxAKhGgaIWiE4v55udacUoVXiLOhyEa5fxZe5dDTptw/M3AnntSlOgWCTGYCFFhdTq/EUtFUudo+eqki/C75lJSUxK233urVFhYWRlRUFElJST6KSgghhBDCd8p7flR0X/Pmzb3a4+Pj+eijj8jPzycgIKDyAxZCiMtYiKbREiMt/5SU0pU7EXIGxVmlcwZFGjqZqnA0DgonkIUiCwX2HEg95N35nzIoFiCwsGbQ+T8DNO82y3kFq81omDX376bzilobCwtYFxW0/qOwddHff9zvQpGvuWsfmZRCB1Rhgkd3OdDyc3DpTnSlu9sAZ2FtJCfgKPpduX93t/2xTAGQhyIPRb764+pweSjP764y7hcD7isZ1kGjjqYRgYE6aEQUjkCr16gFoXEdWL9zPZmZp8vYu/C75FNWVhZhYWHF2sPDw8nMzCxXnw6HA6UUO3ZceAhdZdE0jV4B0bgsUVW+LSH8ldFgYOfOnShVmu8faj457oUoneo69h0OR637ZrK850dZWVlYLBasVqtXe1hYmHvKSGZmmZNPVX1epWkaQS4ngf72HpGRzs5TmQQ7rQQFNqtQVypIVdtzWMt0sXPnTkIqIe7qpOVo7Ny5s8qfKwpobK2Llg0azsrptBKfK9WppjxXynp8aPnu9x5cBghoXIWRlU1E4a3leW0K94geHXeyRtdA1wzousvTrlDoCvRSjePxpnAneC5VHLtULEBQDMdKus8G7NwBBIMluNRdGgtv1ksteB6NwqSY9kdyzFBCmxH3+8uFnjk2wJbpgp07CXGGEBQYVIYofE9zVd05VmnPq/wu+VQViv5R1fUmHmKRbxCFKI3a9OFQjnshSq+qj31N02rV60tNUx3nVVaj/57CWk3+eXVmi5/GLc+V6uevzxWMZncGogbT+GO0kfAdv32OUzXvzaU9r/K7V+OwsDCys7OLtWdmZhIeHl6uPjt37lzRsIQQQgghfKa850dhYWEUFBRgt9u9Rj9lZWWhaVq5zq3kvEoIIYQQf+Z3SdPmzZsXq12QnZ3NmTNnitUrEEIIIYS4HJT3/KjovsOHD3u1JyUl0ahRI6n3JIQQQohK4XfJp379+rF582aysrI8batXr8ZgMNCnTx8fRiaEEEII4RvlPT/q0qULISEhrFq1ytPmcDj45ptv6NevX5XGLIQQQojLh98ln26//XaCg4OZNGkSGzduZMmSJbz22mvcfvvtREdH+zo8IYQQQohqV9rzo7FjxzJo0CDP31arlYkTJzJv3jw++ugjfvzxR5544gkyMjKYMGGCLx6KEEIIIWohTfnh5aQOHTrE888/z2+//UZwcDDDhg3jsccew2Lx38JfQgghhBAVUZrzozFjxpCcnMy6des8bUopZs+ezYIFC0hPTycxMZEnn3xSajcJIYQQotL4ZfJJCCGEEEIIIYQQQvgHv5t2J4QQQgghhBBCCCH8hySfhBBCCCGEEEIIIUSVkeSTEEIIIYQQQgghhKgyknwSQgghhBBCCCGEEFVGkk9CCCGEEEIIIYQQospI8kkIIYQQQgghhBBCVBlJPgm/Mn36dDp37uzrMABYunQpCQkJpKen+zoUIXwuISHhkrelS5cyZswYJk6ceMn+unXrxvTp08sUQ3mPyZr0ugLy2iLE5ezQoUOMGzeOTp060adPH1577TUKCgouus7p06d57bXXGDZsGJ07d6Zfv3488cQTJCcnV1PUtVd59sefffjhhyQkJJTqvU9cWEX2RWpqKn/961/p2bMnHTp0YMiQIXz99ddVHHHtVt79ce7cOZ5++mn69+9Pp06dGDp0KJ999lk1RFx7HT16lKeffpphw4bRpk0bhg4dWqr1lFLMnj2b/v3706FDB2677Ta2bdtWpbGaqrR3IYQQl4WFCxd6/X3bbbcxZswYrzfA2NhYOnTogMEg33sIIcSfZWZmMnbsWJo1a8b06dNJTU3llVdeIT8/n6effvqC6+3evZu1a9dy66230rFjR86dO8e7777LyJEjWb58OZGRkdX4KGqP8u6P8505c4aZM2dSt27dKo62dqvIvjh9+jS33XYbcXFxPP/884SEhHDgwIEyJxHFHyqyPx555BGSkpJ4/PHHadiwIRs2bODZZ5/FaDQyatSoanoEtcuBAwdYv349HTt2RNd1lFKlWm/OnDlMmzaNKVOmkJCQwKeffsr48eP56quvaNKkSZXEKsknIYQQFdapU6dibQ0bNizWLh+ChBCiZJ9//jk5OTnMmDGDiIgIAFwuF//85z+ZOHEi0dHRJa7XtWtXVq1ahcn0x2l9ly5d6N+/P19++SXjx4+vjvBrnfLuj/P961//YsCAAaSkpFRxtLVbRfbFv/71Lxo0aMD777+P0WgEoFevXtURdq1V3v1x5swZtmzZwssvv8zw4cMB977YuXMnK1askORTOQ0YMIBrrrkGgKlTp7Jr165LrmO325k1axbjx4/n7rvvBtzvJYMHD2bu3Lk8++yzVRKrfP0sapXXX3+dG2+8kc6dO3PllVfy+OOPc/r0ac/9H3/8MR07dsRms3mtd+jQIRISEli/fj0AP/zwA+PGjaNXr1506dKFkSNHsmHDhktuf8mSJbRr144vvviich+YELVESdPuvv32WwYPHkz79u0ZMWIEO3bsKLaer49JeW0RQlS1DRs20KtXL8+HOYAhQ4ag6zqbNm264HphYWFeiSeABg0aEBkZ6fU6JcqmvPujyC+//MK3337LE088UYVRXh7Kuy9sNhurVq3izjvv9CSeRMWVd384nU4AQkNDvdpDQkJKPVpHFFeeGQW//vorNpuNIUOGeNosFguDBg0q1XlpeUnySdQqaWlpTJw4kVmzZvH3v/+d5ORkxowZ43mxu+mmm1BKsXz5cq/1Fi9eTHR0NH379gXgxIkTXH311bz22mtMnz6dLl26cN9997Fly5YLbvvjjz/mmWee4dVXX2XkyJFV9yCFqEX27t3Lww8/TLNmzZgxYwa33HILjz76aLHh8L4+JuW1RQhR1ZKSkmjevLlXW1hYGFFRUSQlJZWpr8OHD5OWlkZ8fHxlhnhZqcj+cLlcPP/889x///3Ur1+/KsO8LJR3X+zevRuHw4HJZGL06NG0bduWPn368K9//QuHw1HVYdda5d0fDRs2pG/fvrz33nscPHgQm83GypUr2bRpE3fddVdVhy3OU7Sf/rwf4+PjSUlJIT8/v0q2K9PuRK3y8ssve353uVyewps//fQTffv2JTw8nOuuu44lS5Zw++23A+4s/Ndff82IESM834qMHj3a04+u6/To0YODBw+yaNEievToUWy7s2bNYsaMGbz99tsMHDiwih+lELXH7NmzadiwITNnzvQcf1arlb///e9ey/n6mJTXFiFEVcvKyiIsLKxYe3h4OJmZmaXuRynFCy+8QP369bnhhhsqM8TLSkX2x4IFC8jLy/NMZxEVU959cfbsWQCeeuopRo0axUMPPcSOHTuYNm0aBoNBRqWVU0WOjenTp/PYY495XpuMRiNPPfUU1113XZXEKkqWlZWFxWLBarV6tYeFhaGUIjMzk4CAgErfriSfRK2yfv163n33XQ4cOOA1/eXIkSOekQejRo1i9OjRHDhwgJYtW7J+/XrS0tK49dZbPcufOnWKN998k82bN3PmzBnPUNC2bdsW2+Zbb73FsmXLmDVrFr17967iRyhE7bJ9+3YGDBjgNRx+8ODBxZJPvj4m5bVFCOEvpk+fzk8//cT7779PUFCQr8O57KSlpTFt2jReffVVLBaLr8O5rOm6DkDv3r2ZOnUqAD179iQnJ4d58+YxadKkKvmALUqmlOLJJ5/kyJEjvPHGG0RFRbF582ZeeuklwsPDJVl+GZDkk6g1duzYwYMPPsjAgQO59957qVu3LpqmMWrUKOx2u2e57t27ExcXx+LFi3nyySdZsmQJ3bt3JzY2FnC/UT3wwANkZ2fz8MMP07RpUwIDA5k2bRonT54stt01a9bQqlUrunbtWm2PVYja4syZM8WuAhQSEuL1TYyvj0l5bRFCVIewsDCys7OLtWdmZhIeHl6qPhYtWsTMmTN58cUXpahyBZV3f7z99tskJCTQrVs3srKyAPdIWKfTSVZWFkFBQcVqdImLK+++KBqd07NnT6/2Xr168d5773H06FESEhIqN9jLQHn3xw8//MDq1av5+uuvPf/3Hj16kJaWxiuvvCLJp2oUFhZGQUEBdrvd65w7KysLTdNK/Z5TVlLzSdQa3377LSEhIbz11lsMHDiQTp06Ua9evRKXHTlyJF9//TWnTp1i/fr1XiMTjh49yp49e5g6dSojR47kiiuuoH379hec+/rOO+9w/PhxJk+eLPPHhSijqKgo0tLSvNpsNptXUsfXx6S8tgghqkPz5s2L1UvJzs7mzJkzxepylGTt2rU8++yzPPzww4wYMaKqwrxslHd/HD58mJ9//pnu3bt7br/++isbN26ke/fubN68uapDr3XKuy9atGhx0X7PP9cQpVfe/XHw4EGMRiOtWrXyak9MTOT06dPk5eVVSbyiuKL9dPjwYa/2pKQkGjVqVGUjAiX5JGqN/Px8zGYzmqZ52pYtW1bisrfccgvZ2dlMmTKFgIAABg8e7Lmv6I3IbDZ72pKTk/ntt99K7CsuLo4PPviA7du388QTT+ByuSrj4QhxWejQoQPff/+913GzevVqr2V8fUzKa4sQojr069ePzZs3e0bLgPv10GAw0KdPn4uuu2XLFh5//HFGjhzJpEmTqjrUy0J598ff/vY35s+f73Vr3bo1nTp1Yv78+XTo0KE6wq9VyrsvGjduTKtWrYol/DZv3kxAQMAlk1OiZBXZHy6Xi3379nm17969m7p16xIYGFhlMQtvXbp0ISQkhFWrVnnaHA4H33zzDf369auy7cqYT+F3XC5XsQ+nAG3atOGjjz7i+eefZ9CgQfz222989dVXJfYRGRnJwIEDWb16NbfddptXdrd58+Y0aNCAN954A13Xyc3NZdq0aRe9WklCQgLz5s1j7Nix/PWvf+W1114r12Uvhbjc3HfffYwYMYJJkyZxxx13cOLECebOnes1BLg6jskLva506NCBPn36yGuLEKLK3X777Xz88cdMmjSJiRMnkpqaymuvvcbtt99OdHS0Z7mxY8eSkpLC2rVrATh06BCTJk2iWbNmDBs2jG3btnmWjYyM9Ez9FWVT3v2RmJhYrK+wsDCCgoJKvLCEuLTy7guAxx57jAcffJAXX3yR/v37s3PnTubNm8eECROkJlo5lXd/9OvXj0aNGvHwww8zadIk6tevz8aNG/nPf/7D5MmTffVw/F5eXh7r168H3F9q2mw2zzntFVdcQWRkZLF9YbVamThxItOnTycyMpJWrVrx2WefkZGRwYQJE6osVkk+Cb9jt9t55JFHirW/9tprTJkyhU8++YSlS5fSpUsXZs2adcGrJwwaNIjVq1cXG5pusViYPn06zz33HI888ggNGzbkgQce4KeffmLXrl0XjKtt27a8//77jBs3jqeffprnn3/ea6SEEKK4Nm3a8Pbbb/P666/z0EMP0bJlS958802vN77qOCYv9roybNgweW0RQlS58PBwT6J70qRJBAcHM2LECB577DGv5XRd9xoJuX37drKzs8nOzuaOO+7wWvaWW27hlVdeqZb4a5vy7g9R+SqyLwYMGMC///1v3nnnHT777DPq16/P5MmTue+++6rzIdQq5d0fISEhfPjhh7z55pu8/vrrZGdnExMTw9SpU72uBizKJi0trdg5bNHf8+fPp0ePHiUeG/feey9KKebNm0d6ejqJiYnMnTuXJk2aVFmsmiq61I4Ql5n/+7//Y+/evRecPiOEEOUhry1CCCGEEEJ4k5FP4rKzb98+9u7dy8qVK3nmmWd8HY4QopaQ1xYhhBBCCCFKJskncdl54IEHSE9P5+abb/a6EpUQQlSEvLYIIYQQQghRMpl2J4QQQgghhBBCCCGqjFwyRwghhBBCCCGEEEJUGUk+CSGEEEIIIYQQQogqI8knIYQQQgghhBBCCFFlJPkkhBBCCCGEEEIIIaqMXO1OCCGEEEIIIYQQohY5evQoc+fOZfv27Rw4cIDmzZuzfPnyMvczdepU/vOf/5R43xNPPMF9991Xqn5k5JMQ4rJ14sQJEhISWLp0qa9DEUIIIcRlZsyYMQwdOrRat7llyxYSEhLYsmWLp23q1KkMGDCgWuMQQlS9AwcOsH79epo2bUp8fHy5+3nwwQdZuHCh123s2LEA9OvXr9T9yMgnIYRfWbp0KU8++SSLFy+mffv2nvbs7GzGjRvHvn37mDlzZpleCIUQQghRuxSdLxSxWCw0atSIPn368OCDD1KvXj0fRifOd+7cOWbPns26detISUkhMDCQ9u3bM3r0aK6++mpfh1cu06dPZ8aMGZdc7oorruDjjz+uhojE5WjAgAFcc801gDvJvGvXrnL1ExsbS2xsrFfbG2+8QYsWLWjdunWp+5HkkxDC79lsNsaPH8++ffuYMWOGJJ6EEEIIAcDDDz9MTEwMBQUFbN26lc8++4z169ezfPlyAgMDfR1etevevTs7duzAbDb7OhQAkpKSuPvuu0lPT2f48OG0b9+erKwsli1bxv3338/48eP561//6uswy2zQoEFeH9Zzc3N59tlnGTRoEIMGDfK0SxJUVCWD4dIT3ZRSzJs3j0WLFpGcnEx0dDRjxozh7rvvvuA6qamp/PLLLzzyyCNlikeST0IIv2az2ZgwYQJ79+5lxowZXHXVVb4OSQghhBA1RL9+/TwjpUeOHElERAQffPAB3333XbVPeasJDAYDVqvV12EA4HA4eOSRR8jKyuLTTz+lY8eOnvvuvvtupkyZwrx582jfvj3XX3/9Bfux2+2YzeZSfdCuLq1bt/YaEZKens6zzz5LQkICw4YN82FkQnh78cUX+eKLL7j//vvp2LEjv/76K6+//jpWq5U77rijxHWWL1+OruvccMMNZdpWzTlChRCijHJycrjnnnvYvXs306dPp3///p77UlNTefLJJ+nduzft2rXjhhtuYPHixZfs8/fff2fq1KkMHDiQ9u3b06dPH5588knOnTvntZzNZuPFF19kwIABtGvXjl69ejFu3Dh2795d2Q9TCCGEEJWkZ8+egLvuY5GvvvqK4cOH06FDB6644goee+wxTp486bVeUX2mgwcPMmbMGDp27MiVV17JnDlzim0jOTmZ+++/n06dOtGrVy9eeukl/vvf/xartVTkUn0WFBTw9ttvM3z4cLp27UqnTp248847+emnn4r1tWLFCoYPH07nzp3p0qULN954Ix999JHn/pJqPpVk7ty53H777fTo0YMOHTowfPhwVq9eXWy5hIQEnnvuOb799luGDh3qOefasGHDRfsH+Oabb9i/fz/33nuvV+IJwGg08txzzxEWFsb06dOLxb9ixQrefPNNrrzySjp27IjNZiMjI4NXX32VG2+80fP477nnHn7//Xevvov6WLlyJe+++64nQTl27FiOHj1aLM5PP/2UgQMH0qFDB0aMGMEvv/zCmDFjGDNmzCUf48UsXbqUhIQEr+fi+fGdv49++eUXHn74Yfr370+7du246qqreOmll8jPz69QDOLyduzYMT755BP+9re/8cADD9C7d28eeugh7r77bmbOnImu6yWut3z5cjp37kyTJk3KtD0Z+SSE8Et5eXnce++97Nq1i7fffturJsDZs2cZNWoUmqZx1113ERkZyYYNG/j73/+OzWa76DDSzZs3c/z4cYYPH05UVBQHDhxg0aJFHDx4kEWLFqFpGgDPPPMMa9asYfTo0cTHx5ORkcHWrVs5dOgQbdu2reqHL4QQQohyOHbsGAAREREAvPvuu7z99tsMGTKEESNGkJ6ezieffMJdd93Fl19+SVhYmGfdzMxM7rnnHgYNGsSQIUNYs2YNr7/+Oq1atfKMvM7NzWXs2LGcOXOGv/zlL9SrV4/ly5dfMNlTmj5tNhtffPEFQ4cOZeTIkeTk5LB48WLuuecevvjiCxITEwHYtGkTjz/+OL169WLKlCmAe1rbr7/+6ikOXFrz589nwIAB3HjjjTgcDlasWMEjjzzCrFmzvL7sA9i6dSvffPMNd955J8HBwXz88cc8/PDDfP/999SpU+eC21i3bh0AN998c4n3h4aGMnDgQP7zn/9w9OhRmjZt6rnvnXfewWw2M2HCBAoKCjCbzRw8eJBvv/2WwYMHExMTw9mzZ1m4cCGjR49mxYoVREdHe/U/Z84cNE1j/Pjx2Gw23n//faZMmcIXX3zhWWbBggU899xzdOvWjbvvvpvk5GQmTZpEWFgYDRo0KNP/tCJWr15Nfn4+d9xxBxEREezYsYNPPvmEU6dOMW3atGqLQ9QumzdvBuDaa6/F6XR62nv37s2cOXM4efIkjRs39lrn0KFD7Nmzh3/84x9l3p4kn4QQfmnq1KmcPn2at956i4EDB3rd9+abb+JyuVi2bJnnpOeOO+7g8ccfZ8aMGdx+++0EBASU2O+dd97J+PHjvdo6derE448/ztatW+nWrRsA69evZ9SoUUydOtWz3L333luZD1EIIYQQFWSz2UhPT6egoIBff/2VmTNnEhAQwNVXX01ycjLTp0/n0Ucf5f777/esc+2113LLLbewYMECr/bTp0/z6quvepIlI0aMYMCAASxZssSTKFq4cCHHjx9n5syZnkK/t99++wUTLKXpMzw8nHXr1mGxWDzrjRo1iiFDhvDxxx/z0ksvAfDDDz8QEhLC3LlzMRqNFfq/rVmzxutc6a677mL48OF88MEHxZJPhw4dYuXKlZ4aRz169GDYsGGsWLGC0aNHX3Abhw4dIjQ0tNiH2/MVTV07dOiQV/LJbrezZMkSrxgTEhJYs2aN1/S7YcOGMWTIEBYvXsykSZO8+rbb7Xz55Zee/2tYWBgvvvgi+/fvp1WrVp4RZ+3bt+ejjz7CZDJ5tjN16tRqTT5NmTLF67HedtttNG3alH//+9+kpKTQqFGjaotF1B7nzp1DKeUZEfpnJSWfli1bhslkuuhU2AuR5JMQwi+dPXsWi8VCw4YNvdqVUnzzzTcMGTIEpRTp6eme+/r27cuKFSvYvXs3Xbt2LbHf89/Y7XY7OTk5nqHgu3fv9iSfwsLC2L59O6mpqcW+SRNCCCFEzfDn0c6NGzfm9ddfJzo6mg8//BBd1xkyZIjX+UK9evVo2rQpW7Zs8Uo+BQUFedXrsVgstG/fnuPHj3va/vvf/xIdHe31xZjVamXUqFG88sorxeIrTZ9Go9GTTNJ1naysLHRdp127duzZs8ezXFhYGHl5eWzatKnCF185/3woMzMTl8tF165dWbFiRbFle/fu7VVcu3Xr1oSEhHg9hpLk5OQQHBx80WWK7rfZbF7tN998c7EvEs9PzrlcLrKysggKCiIuLs7r/1Rk+PDhXusUneMdP36cVq1asWvXLjIyMnjiiSc8iSeAG2+8kZdffvmicVe28x9rbm4u+fn5dO7cGaUUe/bskeSTKJfw8HA0TWPBggUlXoQgLi6uWNuKFSvo1asXkZGRZd6eJJ+EEH7pueee4+WXX+aee+7h008/pXnz5oC7oGNWVhYLFy5k4cKFJa57/gnmn2VkZDBjxgxWrlxJWlqa133Z2dme36dMmcLUqVPp378/bdu25aqrruLmm28u89xnIYQQQlSdp59+mri4OIxGI/Xq1SMuLs4zMubIkSMopbj22mtLXPf8hANAgwYNPNPvi4SHh7Nv3z7P38nJycTGxhZb7s+XKS9LnwD/+c9/mDdvHocPH8bhcHjaY2JiPL/feeedrFq1invvvZfo6Gj69OnDkCFDypWI+v7773n33XfZu3cvBQUFnvY/xwoU+yKw6DFkZWVddBvBwcHFamr+WU5OjmfZ853/uIvous78+fNZsGABJ06cwOVyee4rmmZ5vj8nbIqmWBbFnZKSAhTfdyaT6aKjtapCSkoK06ZNY926dWRmZnrd9+fEnBCl1atXL8D9+WfAgAGXXH779u0cO3as2CjC0pLkkxDCL8XHxzNnzhzGjh3L+PHj+eyzz2jYsKGnMN5NN93ELbfcUuK6CQkJF+z30Ucf5bfffmPChAkkJiYSFBSEruvcc889KKU8y11//fV069aNtWvXsmnTJubOncucOXOYPn26XHFPCCGEqCE6dOjgudrdn+m6jqZpzJkzp8RpakFBQV5/V3QqW0lK0+dXX33F1KlTueaaa5gwYQJ169bFaDQya9Ysr9FFdevW5csvv2Tjxo1s2LCBDRs2sHTpUm6++WZeffXVUsf0yy+/8MADD9C9e3eeeeYZoqKiMJvNLFmyhOXLl5f6MZx/3lSS+Ph49u7de9FpY0VJuBYtWni1l1Q+4b333uPtt9/m1ltv5ZFHHiE8PByDwcBLL71UYiwXujrepeKuLCUl8oBiRZ5dLhfjxo3z1Adr3rw5QUFBpKamMnXq1AsWhRYiLy+P9evXA+7EuM1m81w44IorriAuLo677rqL//u//2PChAl07NgRh8PBkSNH2LJlC++8845Xf8uWLSMgIIBBgwaVKx5JPgkh/FaHDh145513uO+++xg3bhwLFiwgMjKS4OBgdF2nd+/eZeovMzOTH3/8kcmTJ/PQQw952o8cOVLi8vXr1+euu+7irrvuIi0tjVtuuYX33ntPkk9CCCGEH4iNjUUpRUxMTInTS8qjcePGHDx4EKWUV3KhqNB5eaxZs4YmTZowY8YMrz5LKjRtsVgYMGAAAwYMQNd1nn32WRYuXMiDDz7oVTPpUtuzWq3MnTvXa1rakiVLyv0YStK/f3+WL1/Ol19+yYMPPljsfpvNxnfffUfz5s1LFfuaNWvo0aOHpwZWkaysrIsWPr+QooTYsWPHvGriOJ1OkpOTL/plZmkUjbQ6f2Q9uJME59u/fz9Hjhzxqg0G7gLzQlxMWloajzzyiFdb0d/z58+nR48ePPXUU8TFxbFw4UJmzpxJcHAwcXFxDB482Gs9l8vF6tWrufrqqy85XfZCSk73CiGEn+jVqxf//ve/OXbsGPfccw95eXlcd911rFmzhv379xdb/mJT7i70zd35lygG94vvn08U6tatS/369b2GpgshhBCi5rr22msxGo3MmDGj2GgXpdQlp4SVpG/fvqSmpvLdd9952ux2O4sWLSp3nEXnJ+fHuH37drZt2+a13J/jNRgMngRJWc5PjEYjmqZ5TVs7ceKE12OqDNdddx0tWrRgzpw57Ny50+s+Xdd55plnyMzM9PpC8FJx/3k/rlq1itTU1HLF165dOyIiIli0aJHXlcCWLVtWbOpbeRRN5/v55589bS6Xq9hzpWiE1vmPTSnF/PnzKxyDqN1iYmLYt29fibcePXoA7hF4o0ePZtmyZezatYstW7bw+eefF6uXZzQa2bhxI2+99Va545GRT0IIvzdo0CCef/55/va3v/HAAw/wyiuvsGXLFkaNGsXIkSNp0aIFmZmZ7N69mx9//JH//e9/JfYTEhJC9+7def/993E4HERHR7Np0yZOnDjhtVxOTg5XXXUV1113Ha1btyYoKIjNmzezc+dOr6vfCSGEEKLmio2N5dFHH+WNN94gOTmZa665huDgYE6cOMG3337LqFGjmDBhQpn6vO222/jkk0944okn+Mtf/kJUVBTLli3DarUCF55qdTH9+/fnm2++YdKkSfTv358TJ07w+eef06JFC3Jzcz3LPfXUU2RmZtKzZ0+io6NJSUnhk08+ITExkfj4+FJv76qrruKDDz7gnnvuYejQoaSlpbFgwQJiY2OL1aKqCIvFwrRp0xg7dix33nknw4cPp127dmRnZ7N8+XJ2797N+PHjueGGG0rVX//+/Zk5cyZPPvkknTt3Zv/+/Sxbtqzc9TgtFguTJ0/m+eefZ+zYsQwZMoTk5GSWLl16wRpeZdGyZUs6derEv//9bzIzMwkPD2flypVeiS6A5s2bExsby6uvvkpqaiohISGsWbPmkjW1hKhpJPkkhKgVbr31VjIzM3n11Vd5/vnn+eKLL5g5cyZr167ls88+IyIighYtWjBlypSL9vPGG2/w/PPPs2DBApRS9OnThzlz5nDllVd6lgkICOCOO+5g06ZNfPPNNyiliI2N5ZlnnuHOO++s6ocqhBBCiEpy33330axZMz788ENmzpwJuIuA9+nTp1QFeP8sODiYjz76iBdeeIH58+cTFBTEzTffTOfOnZk8ebInCVUWw4cP5+zZsyxcuJCNGzfSokUL/vWvf7F69WqvL9RuuukmFi1axIIFC8jKyiIqKoohQ4YwefLkC9Y3KkmvXr148cUXmTNnDi+99BIxMTFMmTKF5OTkSk0+gbvu09dff83s2bNZt24dS5cuJSAggHbt2vHuu++WaR/cf//95OXlsWzZMlauXEmbNm2YNWsWb7zxRrnjGz16NEopPvjgA1599VVat27Nu+++ywsvvFCufflnr7/+Ok8//TSzZ88mLCyMESNG0KNHD8aNG+dZxmw289577/HCCy8wa9YsrFYrgwYN4q677vK6UqIQNZ2mqquimhBCCCGEEEJchj788ENefvllNmzYQHR0tK/DERWg6zq9evVi0KBBvPDCC74ORwi/ITWfhBBCCCGEEKKS5Ofne/1tt9tZuHAhzZo1k8STn7Hb7cXqSH355ZdkZGRwxRVX+CgqIfyTTLsTQgghhBBCiEry0EMP0ahRI1q3bo3NZuPrr78mKSmJ119/3dehiTLatm0bL7/8MoMHDyYiIoI9e/awePFiWrVqVexqYEKIi5PkkxBCCCGEEEJUkr59+7J48WKWLVuGy+WiRYsWvPnmm1x//fW+Dk2UUePGjWnQoAEff/yxpyj4sGHDmDJlChaLxdfhCeFXpOaTEEIIIYQQQgghhKgyUvNJCCGEEEIIIYQQQlQZST4JIYQQQgghhBBCiCojySchhBBCCCGEEEIIUWUk+SSEEEIIIYQQQgghqowkn4QQQgghhBBCCCFElZHkkxBCCCGEEEIIIYSoMpJ8EkIIIYQQQgghhBBVRpJPQgghhBBCCCGEEKLKSPJJCCGEEEIIIYQQQlSZ/w8+8R4Tx06I4wAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+        "\n",
+        "sns.countplot(data=df, x='Label Kelayakan', ax=axes[0])\n",
+        "axes[0].set_title('Distribusi Label Kelayakan UKT')\n",
+        "axes[0].set_xlabel('Kelas')\n",
+        "axes[0].set_ylabel('Jumlah')\n",
+        "\n",
+        "sns.histplot(data=df, x='Penghasilan Orang Tua', hue='Label Kelayakan', kde=True, bins=10, ax=axes[1])\n",
+        "axes[1].set_title('Penghasilan Orang Tua berdasarkan Label')\n",
+        "axes[1].set_xlabel('Penghasilan Orang Tua')\n",
+        "axes[1].set_ylabel('Jumlah')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "id": "gGziaQlXlYk_"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 367
+        },
+        "id": "IbipDqYMlYk_",
+        "outputId": "64e48762-786b-4864-aa19-6fb49e2fbbfa"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+        "\n",
+        "sns.countplot(data=df, x='Jumlah Tanggungan Orang Tua', hue='Label Kelayakan', ax=axes[0])\n",
+        "axes[0].set_title('Jumlah Tanggungan Orang Tua')\n",
+        "axes[0].set_xlabel('Jumlah Tanggungan')\n",
+        "axes[0].set_ylabel('Jumlah')\n",
+        "\n",
+        "sns.countplot(data=df, x='Kendaraan', hue='Label Kelayakan', ax=axes[1])\n",
+        "axes[1].set_title('Jumlah Kendaraan')\n",
+        "axes[1].set_xlabel('Jumlah Kendaraan')\n",
+        "axes[1].set_ylabel('Jumlah')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "id": "IbipDqYMlYk_"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 458
+        },
+        "id": "BqXGfcETlYk_",
+        "outputId": "3afe6864-9c39-4daa-8088-7fb8a6b403e0"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x500 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(12, 5))\n",
+        "sns.countplot(data=df, y='Pekerjaan Orang Tua', hue='Label Kelayakan', order=df['Pekerjaan Orang Tua'].value_counts().index)\n",
+        "plt.title('Kelayakan UKT berdasarkan Pekerjaan Orang Tua')\n",
+        "plt.xlabel('Jumlah')\n",
+        "plt.ylabel('Pekerjaan Orang Tua')\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "id": "BqXGfcETlYk_"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 161
+        },
+        "id": "gKpe3OQqlYk_",
+        "outputId": "fa3bc1c9-b8d7-4f6c-bcf5-8b3848c3c55c"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "                          jumlah_data  rata_rata_penghasilan  \\\n",
+              "Kelayakan Keringanan UKT                                       \n",
+              "Layak                              35             3591428.57   \n",
+              "Tidak Layak                        65             6415384.62   \n",
+              "\n",
+              "                          median_penghasilan  rata_rata_tanggungan  \\\n",
+              "Kelayakan Keringanan UKT                                             \n",
+              "Layak                              3000000.0                  3.80   \n",
+              "Tidak Layak                        7000000.0                  1.94   \n",
+              "\n",
+              "                          median_tanggungan  rata_rata_kendaraan  \n",
+              "Kelayakan Keringanan UKT                                          \n",
+              "Layak                                   4.0                 0.63  \n",
+              "Tidak Layak                             2.0                 1.31  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-274e132b-d896-4ff2-be92-acf8a0778550\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>jumlah_data</th>\n",
+              "      <th>rata_rata_penghasilan</th>\n",
+              "      <th>median_penghasilan</th>\n",
+              "      <th>rata_rata_tanggungan</th>\n",
+              "      <th>median_tanggungan</th>\n",
+              "      <th>rata_rata_kendaraan</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Kelayakan Keringanan UKT</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Layak</th>\n",
+              "      <td>35</td>\n",
+              "      <td>3591428.57</td>\n",
+              "      <td>3000000.0</td>\n",
+              "      <td>3.80</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>0.63</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Tidak Layak</th>\n",
+              "      <td>65</td>\n",
+              "      <td>6415384.62</td>\n",
+              "      <td>7000000.0</td>\n",
+              "      <td>1.94</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.31</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-274e132b-d896-4ff2-be92-acf8a0778550')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-274e132b-d896-4ff2-be92-acf8a0778550 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-274e132b-d896-4ff2-be92-acf8a0778550');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "  <div id=\"id_9298679d-dcb3-4ed4-b909-24c4b1d59efc\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('ringkasan_label')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_9298679d-dcb3-4ed4-b909-24c4b1d59efc button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('ringkasan_label');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "ringkasan_label",
+              "summary": "{\n  \"name\": \"ringkasan_label\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Kelayakan Keringanan UKT\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Tidak Layak\",\n          \"Layak\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"jumlah_data\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21,\n        \"min\": 35,\n        \"max\": 65,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          65,\n          35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rata_rata_penghasilan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1996838.4727277772,\n        \"min\": 3591428.57,\n        \"max\": 6415384.62,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          6415384.62,\n          3591428.57\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_penghasilan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2828427.12474619,\n        \"min\": 3000000.0,\n        \"max\": 7000000.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          7000000.0,\n          3000000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rata_rata_tanggungan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3152186130069783,\n        \"min\": 1.94,\n        \"max\": 3.8,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.94,\n          3.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_tanggungan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4142135623730951,\n        \"min\": 2.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2.0,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rata_rata_kendaraan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.48083261120685233,\n        \"min\": 0.63,\n        \"max\": 1.31,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.31,\n          0.63\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "ringkasan_label = df.groupby('Kelayakan Keringanan UKT').agg(\n",
+        "    jumlah_data=('Kelayakan Keringanan UKT', 'size'),\n",
+        "    rata_rata_penghasilan=('Penghasilan Orang Tua', 'mean'),\n",
+        "    median_penghasilan=('Penghasilan Orang Tua', 'median'),\n",
+        "    rata_rata_tanggungan=('Jumlah Tanggungan Orang Tua', 'mean'),\n",
+        "    median_tanggungan=('Jumlah Tanggungan Orang Tua', 'median'),\n",
+        "    rata_rata_kendaraan=('Kendaraan', 'mean')\n",
+        ").round(2)\n",
+        "\n",
+        "ringkasan_label.index = ringkasan_label.index.map(label_map)\n",
+        "display(ringkasan_label)\n"
+      ],
+      "id": "gKpe3OQqlYk_"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 521
+        },
+        "id": "Ve5hO1tQlYk_",
+        "outputId": "0f3f1bcc-6492-4b4f-eec9-aca17c0df219"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Label Kelayakan      Layak  Tidak Layak  All\n",
+              "Pekerjaan Orang Tua                         \n",
+              "Buruh                    1           11   12\n",
+              "Guru                     0           12   12\n",
+              "Ibu Rumah Tangga        12            6   18\n",
+              "Nelayan                  7            0    7\n",
+              "PNS                      1           12   13\n",
+              "Petani                  13            0   13\n",
+              "TNI/POLRI                0           13   13\n",
+              "Wiraswasta               1           11   12\n",
+              "All                     35           65  100"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4c17bfb9-f379-475c-960a-3862ab67d891\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>Label Kelayakan</th>\n",
+              "      <th>Layak</th>\n",
+              "      <th>Tidak Layak</th>\n",
+              "      <th>All</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Pekerjaan Orang Tua</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Buruh</th>\n",
+              "      <td>1</td>\n",
+              "      <td>11</td>\n",
+              "      <td>12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Guru</th>\n",
+              "      <td>0</td>\n",
+              "      <td>12</td>\n",
+              "      <td>12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Ibu Rumah Tangga</th>\n",
+              "      <td>12</td>\n",
+              "      <td>6</td>\n",
+              "      <td>18</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Nelayan</th>\n",
+              "      <td>7</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>PNS</th>\n",
+              "      <td>1</td>\n",
+              "      <td>12</td>\n",
+              "      <td>13</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Petani</th>\n",
+              "      <td>13</td>\n",
+              "      <td>0</td>\n",
+              "      <td>13</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>TNI/POLRI</th>\n",
+              "      <td>0</td>\n",
+              "      <td>13</td>\n",
+              "      <td>13</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Wiraswasta</th>\n",
+              "      <td>1</td>\n",
+              "      <td>11</td>\n",
+              "      <td>12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>All</th>\n",
+              "      <td>35</td>\n",
+              "      <td>65</td>\n",
+              "      <td>100</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4c17bfb9-f379-475c-960a-3862ab67d891')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4c17bfb9-f379-475c-960a-3862ab67d891 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-4c17bfb9-f379-475c-960a-3862ab67d891');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "  <div id=\"id_45919b77-4b72-4953-9634-76ebe2d64c9d\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('crosstab_pekerjaan')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_45919b77-4b72-4953-9634-76ebe2d64c9d button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('crosstab_pekerjaan');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "crosstab_pekerjaan",
+              "summary": "{\n  \"name\": \"crosstab_pekerjaan\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"Pekerjaan Orang Tua\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Wiraswasta\",\n          \"Guru\",\n          \"Petani\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Layak\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 0,\n        \"max\": 35,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1,\n          0,\n          35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tidak Layak\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 65,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          11,\n          12,\n          65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"All\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 7,\n        \"max\": 100,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          18,\n          100,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Label Kelayakan  Layak  Tidak Layak  All\n",
+              "Tempat Tinggal                          \n",
+              "0                   19           31   50\n",
+              "1                   16           34   50\n",
+              "All                 35           65  100"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-0c6196df-9856-4bd3-ad14-ab8288f2f223\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>Label Kelayakan</th>\n",
+              "      <th>Layak</th>\n",
+              "      <th>Tidak Layak</th>\n",
+              "      <th>All</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Tempat Tinggal</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>19</td>\n",
+              "      <td>31</td>\n",
+              "      <td>50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>16</td>\n",
+              "      <td>34</td>\n",
+              "      <td>50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>All</th>\n",
+              "      <td>35</td>\n",
+              "      <td>65</td>\n",
+              "      <td>100</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0c6196df-9856-4bd3-ad14-ab8288f2f223')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-0c6196df-9856-4bd3-ad14-ab8288f2f223 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-0c6196df-9856-4bd3-ad14-ab8288f2f223');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "  <div id=\"id_d5b4ed6d-b0f9-4731-916f-80c930917bdf\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('crosstab_tempat')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_d5b4ed6d-b0f9-4731-916f-80c930917bdf button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('crosstab_tempat');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "crosstab_tempat",
+              "summary": "{\n  \"name\": \"crosstab_tempat\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Tempat Tinggal\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          \"All\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Layak\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 16,\n        \"max\": 35,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          19,\n          16,\n          35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tidak Layak\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 31,\n        \"max\": 65,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          31,\n          34,\n          65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"All\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28,\n        \"min\": 50,\n        \"max\": 100,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          100,\n          50\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "crosstab_pekerjaan = pd.crosstab(df['Pekerjaan Orang Tua'], df['Label Kelayakan'], margins=True)\n",
+        "display(crosstab_pekerjaan)\n",
+        "\n",
+        "crosstab_tempat = pd.crosstab(df['Tempat Tinggal'], df['Label Kelayakan'], margins=True)\n",
+        "display(crosstab_tempat)\n"
+      ],
+      "id": "Ve5hO1tQlYk_"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w8h8Pf6rlYlA"
+      },
+      "source": [
+        "## 8. Persiapan Data Modeling\n",
+        "\n",
+        "Fitur yang digunakan:\n",
+        "\n",
+        "- `Tempat Tinggal`\n",
+        "- `Pekerjaan Orang Tua`\n",
+        "- `Penghasilan Orang Tua`\n",
+        "- `Jumlah Tanggungan Orang Tua`\n",
+        "- `Kendaraan`\n",
+        "\n",
+        "Target yang diprediksi adalah `Kelayakan Keringanan UKT`.\n"
+      ],
+      "id": "w8h8Pf6rlYlA"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eN4ruHB1lYlA",
+        "outputId": "77e28fae-7a7b-4d95-c52c-03a5150f59de"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Jumlah data latih: 80\n",
+            "Jumlah data uji: 20\n"
+          ]
+        }
+      ],
+      "source": [
+        "target = 'Kelayakan Keringanan UKT'\n",
+        "features = [\n",
+        "    'Tempat Tinggal',\n",
+        "    'Pekerjaan Orang Tua',\n",
+        "    'Penghasilan Orang Tua',\n",
+        "    'Jumlah Tanggungan Orang Tua',\n",
+        "    'Kendaraan'\n",
+        "]\n",
+        "\n",
+        "X = df[features]\n",
+        "y = df[target]\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(\n",
+        "    X,\n",
+        "    y,\n",
+        "    test_size=0.2,\n",
+        "    random_state=42,\n",
+        "    stratify=y\n",
+        ")\n",
+        "\n",
+        "print('Jumlah data latih:', X_train.shape[0])\n",
+        "print('Jumlah data uji:', X_test.shape[0])\n"
+      ],
+      "id": "eN4ruHB1lYlA"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "iGJ49f7vlYlA"
+      },
+      "outputs": [],
+      "source": [
+        "def make_preprocessor():\n",
+        "    try:\n",
+        "        onehot = OneHotEncoder(handle_unknown='ignore', sparse_output=False)\n",
+        "    except TypeError:\n",
+        "        onehot = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
+        "\n",
+        "    categorical_features = ['Pekerjaan Orang Tua']\n",
+        "    numeric_features = ['Tempat Tinggal', 'Penghasilan Orang Tua', 'Jumlah Tanggungan Orang Tua', 'Kendaraan']\n",
+        "\n",
+        "    return ColumnTransformer(\n",
+        "        transformers=[\n",
+        "            ('cat', onehot, categorical_features),\n",
+        "            ('num', 'passthrough', numeric_features)\n",
+        "        ],\n",
+        "        remainder='drop'\n",
+        "    )\n"
+      ],
+      "id": "iGJ49f7vlYlA"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7UIEq8HZlYlA"
+      },
+      "source": [
+        "## 9. Model 1: Decision Tree\n",
+        "\n",
+        "Decision Tree dipilih karena hasilnya mudah dijelaskan dalam bentuk aturan keputusan. Ini cocok untuk kasus kelayakan bantuan/keringanan yang membutuhkan interpretasi.\n"
+      ],
+      "id": "7UIEq8HZlYlA"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xJaK-pcslYlA",
+        "outputId": "f13682f4-6ad6-4af1-ddd1-8f9445deedaa"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "       Layak       1.00      0.86      0.92         7\n",
+            " Tidak Layak       0.93      1.00      0.96        13\n",
+            "\n",
+            "    accuracy                           0.95        20\n",
+            "   macro avg       0.96      0.93      0.94        20\n",
+            "weighted avg       0.95      0.95      0.95        20\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "decision_tree_model = Pipeline(steps=[\n",
+        "    ('preprocess', make_preprocessor()),\n",
+        "    ('model', DecisionTreeClassifier(\n",
+        "        max_depth=4,\n",
+        "        random_state=42,\n",
+        "        class_weight='balanced'\n",
+        "    ))\n",
+        "])\n",
+        "\n",
+        "decision_tree_model.fit(X_train, y_train)\n",
+        "y_pred_dt = decision_tree_model.predict(X_test)\n",
+        "\n",
+        "print(classification_report(y_test, y_pred_dt, target_names=['Layak', 'Tidak Layak'], zero_division=0))\n"
+      ],
+      "id": "xJaK-pcslYlA"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 480
+        },
+        "id": "y3fh919BlYlA",
+        "outputId": "db6de28c-7d0e-4db4-eb27-74d18cfea99d"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "cm_dt = confusion_matrix(y_test, y_pred_dt)\n",
+        "disp_dt = ConfusionMatrixDisplay(confusion_matrix=cm_dt, display_labels=['Layak', 'Tidak Layak'])\n",
+        "disp_dt.plot(cmap='Blues')\n",
+        "plt.title('Confusion Matrix - Decision Tree')\n",
+        "plt.show()\n"
+      ],
+      "id": "y3fh919BlYlA"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 910
+        },
+        "id": "vL_HLoEklYlA",
+        "outputId": "55d6a61e-5cb3-4508-d05b-7618afdd8641"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "                                        fitur  importance\n",
+              "10           num__Jumlah Tanggungan Orang Tua    0.894933\n",
+              "9                  num__Penghasilan Orang Tua    0.073882\n",
+              "8                         num__Tempat Tinggal    0.029594\n",
+              "2   cat__Pekerjaan Orang Tua_Ibu Rumah Tangga    0.001592\n",
+              "3            cat__Pekerjaan Orang Tua_Nelayan    0.000000\n",
+              "1               cat__Pekerjaan Orang Tua_Guru    0.000000\n",
+              "0              cat__Pekerjaan Orang Tua_Buruh    0.000000\n",
+              "4                cat__Pekerjaan Orang Tua_PNS    0.000000\n",
+              "7         cat__Pekerjaan Orang Tua_Wiraswasta    0.000000\n",
+              "6          cat__Pekerjaan Orang Tua_TNI/POLRI    0.000000\n",
+              "5             cat__Pekerjaan Orang Tua_Petani    0.000000\n",
+              "11                             num__Kendaraan    0.000000"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-be08db53-2c90-4e9c-ab68-be9987761571\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>fitur</th>\n",
+              "      <th>importance</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>num__Jumlah Tanggungan Orang Tua</td>\n",
+              "      <td>0.894933</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>num__Penghasilan Orang Tua</td>\n",
+              "      <td>0.073882</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>num__Tempat Tinggal</td>\n",
+              "      <td>0.029594</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>cat__Pekerjaan Orang Tua_Ibu Rumah Tangga</td>\n",
+              "      <td>0.001592</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>cat__Pekerjaan Orang Tua_Nelayan</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>cat__Pekerjaan Orang Tua_Guru</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>cat__Pekerjaan Orang Tua_Buruh</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>cat__Pekerjaan Orang Tua_PNS</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>cat__Pekerjaan Orang Tua_Wiraswasta</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>cat__Pekerjaan Orang Tua_TNI/POLRI</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>cat__Pekerjaan Orang Tua_Petani</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>num__Kendaraan</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
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+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-be08db53-2c90-4e9c-ab68-be9987761571 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-be08db53-2c90-4e9c-ab68-be9987761571');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "  <div id=\"id_fe03e094-984b-4547-a51e-b885d7e8b4f2\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('importance_df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_fe03e094-984b-4547-a51e-b885d7e8b4f2 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('importance_df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "importance_df",
+              "summary": "{\n  \"name\": \"importance_df\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"fitur\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"cat__Pekerjaan Orang Tua_Petani\",\n          \"cat__Pekerjaan Orang Tua_TNI/POLRI\",\n          \"num__Jumlah Tanggungan Orang Tua\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.25653429813313094,\n        \"min\": 0.0,\n        \"max\": 0.8949328500975071,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.07388166577520353,\n          0.0,\n          0.029593933210829388\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "feature_names = decision_tree_model.named_steps['preprocess'].get_feature_names_out()\n",
+        "tree_classifier = decision_tree_model.named_steps['model']\n",
+        "\n",
+        "importance_df = pd.DataFrame({\n",
+        "    'fitur': feature_names,\n",
+        "    'importance': tree_classifier.feature_importances_\n",
+        "}).sort_values('importance', ascending=False)\n",
+        "\n",
+        "display(importance_df)\n",
+        "\n",
+        "plt.figure(figsize=(10, 5))\n",
+        "sns.barplot(data=importance_df.head(10), x='importance', y='fitur')\n",
+        "plt.title('Feature Importance - Decision Tree')\n",
+        "plt.xlabel('Importance')\n",
+        "plt.ylabel('Fitur')\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "id": "vL_HLoEklYlA"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 519
+        },
+        "id": "hoXn-l7WlYlA",
+        "outputId": "7c5edfd5-1958-4f33-8c3d-a8ef7ce6c09d"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(18, 8))\n",
+        "plot_tree(\n",
+        "    tree_classifier,\n",
+        "    feature_names=feature_names,\n",
+        "    class_names=['Layak', 'Tidak Layak'],\n",
+        "    filled=True,\n",
+        "    rounded=True,\n",
+        "    fontsize=8\n",
+        ")\n",
+        "plt.title('Visualisasi Pohon Keputusan')\n",
+        "plt.show()\n"
+      ],
+      "id": "hoXn-l7WlYlA"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wIgu9HStlYlA"
+      },
+      "source": [
+        "## 10. Model 2: Naive Bayes\n",
+        "\n",
+        "Naive Bayes digunakan sebagai pembanding karena sederhana, cepat, dan sering dipakai untuk klasifikasi dasar. Pada notebook ini digunakan `GaussianNB` setelah fitur kategorikal diubah menjadi one-hot encoding.\n"
+      ],
+      "id": "wIgu9HStlYlA"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FZgNYLRVlYlA",
+        "outputId": "4bd3462b-1f8d-443b-89c7-9292042dd115"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "       Layak       0.86      0.86      0.86         7\n",
+            " Tidak Layak       0.92      0.92      0.92        13\n",
+            "\n",
+            "    accuracy                           0.90        20\n",
+            "   macro avg       0.89      0.89      0.89        20\n",
+            "weighted avg       0.90      0.90      0.90        20\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "naive_bayes_model = Pipeline(steps=[\n",
+        "    ('preprocess', make_preprocessor()),\n",
+        "    ('model', GaussianNB())\n",
+        "])\n",
+        "\n",
+        "naive_bayes_model.fit(X_train, y_train)\n",
+        "y_pred_nb = naive_bayes_model.predict(X_test)\n",
+        "\n",
+        "print(classification_report(y_test, y_pred_nb, target_names=['Layak', 'Tidak Layak'], zero_division=0))\n"
+      ],
+      "id": "FZgNYLRVlYlA"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 480
+        },
+        "id": "f574a6rklYlA",
+        "outputId": "efba65f8-885a-46e8-fb27-d0aa23604225"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "cm_nb = confusion_matrix(y_test, y_pred_nb)\n",
+        "disp_nb = ConfusionMatrixDisplay(confusion_matrix=cm_nb, display_labels=['Layak', 'Tidak Layak'])\n",
+        "disp_nb.plot(cmap='Greens')\n",
+        "plt.title('Confusion Matrix - Naive Bayes')\n",
+        "plt.show()\n"
+      ],
+      "id": "f574a6rklYlA"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oZChfyqTlYlB"
+      },
+      "source": [
+        "## 11. Perbandingan Model\n"
+      ],
+      "id": "oZChfyqTlYlB"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 112
+        },
+        "id": "YDmvpL7klYlB",
+        "outputId": "db1b28ee-2f9c-4b12-a72d-e57246a5a9d5"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "           Model  Accuracy  Precision  Recall  F1-Score\n",
+              "0  Decision Tree      0.95     0.9536    0.95     0.949\n",
+              "1    Naive Bayes      0.90     0.9000    0.90     0.900"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4bc0c29b-9e6c-42db-b4a7-c95f5952bc37\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Model</th>\n",
+              "      <th>Accuracy</th>\n",
+              "      <th>Precision</th>\n",
+              "      <th>Recall</th>\n",
+              "      <th>F1-Score</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Decision Tree</td>\n",
+              "      <td>0.95</td>\n",
+              "      <td>0.9536</td>\n",
+              "      <td>0.95</td>\n",
+              "      <td>0.949</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Naive Bayes</td>\n",
+              "      <td>0.90</td>\n",
+              "      <td>0.9000</td>\n",
+              "      <td>0.90</td>\n",
+              "      <td>0.900</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4bc0c29b-9e6c-42db-b4a7-c95f5952bc37')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4bc0c29b-9e6c-42db-b4a7-c95f5952bc37 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-4bc0c29b-9e6c-42db-b4a7-c95f5952bc37');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(summary_df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Naive Bayes\",\n          \"Decision Tree\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03535533905932733,\n        \"min\": 0.9,\n        \"max\": 0.95,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9,\n          0.95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03790092347159894,\n        \"min\": 0.9,\n        \"max\": 0.9536,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9,\n          0.9536\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03535533905932733,\n        \"min\": 0.9,\n        \"max\": 0.95,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9,\n          0.95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1-Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03464823227814078,\n        \"min\": 0.9,\n        \"max\": 0.949,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9,\n          0.949\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "def evaluate_model(nama_model, y_true, y_pred):\n",
+        "    return {\n",
+        "        'Model': nama_model,\n",
+        "        'Accuracy': accuracy_score(y_true, y_pred),\n",
+        "        'Precision': precision_score(y_true, y_pred, average='weighted', zero_division=0),\n",
+        "        'Recall': recall_score(y_true, y_pred, average='weighted', zero_division=0),\n",
+        "        'F1-Score': f1_score(y_true, y_pred, average='weighted', zero_division=0)\n",
+        "    }\n",
+        "\n",
+        "summary_df = pd.DataFrame([\n",
+        "    evaluate_model('Decision Tree', y_test, y_pred_dt),\n",
+        "    evaluate_model('Naive Bayes', y_test, y_pred_nb)\n",
+        "]).round(4)\n",
+        "\n",
+        "display(summary_df.sort_values('F1-Score', ascending=False))\n"
+      ],
+      "id": "YDmvpL7klYlB"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 112
+        },
+        "id": "1TJL0ZLClYlB",
+        "outputId": "6b730d33-d260-4c5e-a5b0-f66dabffdab9"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "           Model  CV Accuracy Mean  CV Accuracy Std\n",
+              "0  Decision Tree              0.95           0.0632\n",
+              "1    Naive Bayes              0.85           0.1304"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-5645c88a-06b3-48ef-893a-aafdb1169413\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Model</th>\n",
+              "      <th>CV Accuracy Mean</th>\n",
+              "      <th>CV Accuracy Std</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Decision Tree</td>\n",
+              "      <td>0.95</td>\n",
+              "      <td>0.0632</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Naive Bayes</td>\n",
+              "      <td>0.85</td>\n",
+              "      <td>0.1304</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5645c88a-06b3-48ef-893a-aafdb1169413')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-5645c88a-06b3-48ef-893a-aafdb1169413 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-5645c88a-06b3-48ef-893a-aafdb1169413');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "  <div id=\"id_d5ec5f04-9675-4367-8392-929525c07b15\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('cv_results')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_d5ec5f04-9675-4367-8392-929525c07b15 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('cv_results');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "cv_results",
+              "summary": "{\n  \"name\": \"cv_results\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Naive Bayes\",\n          \"Decision Tree\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CV Accuracy Mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07071067811865474,\n        \"min\": 0.85,\n        \"max\": 0.95,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.85,\n          0.95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CV Accuracy Std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04751757569573598,\n        \"min\": 0.0632,\n        \"max\": 0.1304,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.1304,\n          0.0632\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
+        "\n",
+        "cv_results = pd.DataFrame({\n",
+        "    'Model': ['Decision Tree', 'Naive Bayes'],\n",
+        "    'CV Accuracy Mean': [\n",
+        "        cross_val_score(decision_tree_model, X, y, cv=cv, scoring='accuracy').mean(),\n",
+        "        cross_val_score(naive_bayes_model, X, y, cv=cv, scoring='accuracy').mean()\n",
+        "    ],\n",
+        "    'CV Accuracy Std': [\n",
+        "        cross_val_score(decision_tree_model, X, y, cv=cv, scoring='accuracy').std(),\n",
+        "        cross_val_score(naive_bayes_model, X, y, cv=cv, scoring='accuracy').std()\n",
+        "    ]\n",
+        "}).round(4)\n",
+        "\n",
+        "display(cv_results)\n"
+      ],
+      "id": "1TJL0ZLClYlB"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 401
+        },
+        "id": "n3bDeTpMlYlB",
+        "outputId": "9ac83009-33ad-48ae-cd32-b0a913e7e13d"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(8, 4))\n",
+        "plot_df = summary_df.melt(id_vars='Model', value_vars=['Accuracy', 'Precision', 'Recall', 'F1-Score'], var_name='Metrik', value_name='Nilai')\n",
+        "sns.barplot(data=plot_df, x='Metrik', y='Nilai', hue='Model')\n",
+        "plt.ylim(0, 1)\n",
+        "plt.title('Perbandingan Performa Model')\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "id": "n3bDeTpMlYlB"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cXuyXjhglYlB"
+      },
+      "source": [
+        "## 12. Contoh Prediksi Data Baru\n"
+      ],
+      "id": "cXuyXjhglYlB"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 130
+        },
+        "id": "xhSEJoi1lYlB",
+        "outputId": "57713ec3-380e-4f1b-a36f-eb3d4b17e986"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   Tempat Tinggal Pekerjaan Orang Tua  Penghasilan Orang Tua  \\\n",
+              "0               1              Petani                2500000   \n",
+              "1               0                 PNS                9000000   \n",
+              "\n",
+              "   Jumlah Tanggungan Orang Tua  Kendaraan Prediksi Decision Tree  \\\n",
+              "0                            4          0                  Layak   \n",
+              "1                            2          2            Tidak Layak   \n",
+              "\n",
+              "  Prediksi Naive Bayes  \n",
+              "0                Layak  \n",
+              "1          Tidak Layak  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b74f9516-a635-4b56-b94c-11b3bb73c131\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Tempat Tinggal</th>\n",
+              "      <th>Pekerjaan Orang Tua</th>\n",
+              "      <th>Penghasilan Orang Tua</th>\n",
+              "      <th>Jumlah Tanggungan Orang Tua</th>\n",
+              "      <th>Kendaraan</th>\n",
+              "      <th>Prediksi Decision Tree</th>\n",
+              "      <th>Prediksi Naive Bayes</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Petani</td>\n",
+              "      <td>2500000</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Layak</td>\n",
+              "      <td>Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0</td>\n",
+              "      <td>PNS</td>\n",
+              "      <td>9000000</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b74f9516-a635-4b56-b94c-11b3bb73c131')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b74f9516-a635-4b56-b94c-11b3bb73c131 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b74f9516-a635-4b56-b94c-11b3bb73c131');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "  <div id=\"id_7459c19f-41a4-41df-996c-3e8b514f15d2\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('hasil_prediksi')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_7459c19f-41a4-41df-996c-3e8b514f15d2 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('hasil_prediksi');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "hasil_prediksi",
+              "summary": "{\n  \"name\": \"hasil_prediksi\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Tempat Tinggal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pekerjaan Orang Tua\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"PNS\",\n          \"Petani\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Penghasilan Orang Tua\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4596194,\n        \"min\": 2500000,\n        \"max\": 9000000,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          9000000,\n          2500000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jumlah Tanggungan Orang Tua\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Kendaraan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Prediksi Decision Tree\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Tidak Layak\",\n          \"Layak\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Prediksi Naive Bayes\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Tidak Layak\",\n          \"Layak\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "contoh_mahasiswa = pd.DataFrame([\n",
+        "    {\n",
+        "        'Tempat Tinggal': 1,\n",
+        "        'Pekerjaan Orang Tua': 'Petani',\n",
+        "        'Penghasilan Orang Tua': 2500000,\n",
+        "        'Jumlah Tanggungan Orang Tua': 4,\n",
+        "        'Kendaraan': 0\n",
+        "    },\n",
+        "    {\n",
+        "        'Tempat Tinggal': 0,\n",
+        "        'Pekerjaan Orang Tua': 'PNS',\n",
+        "        'Penghasilan Orang Tua': 9000000,\n",
+        "        'Jumlah Tanggungan Orang Tua': 2,\n",
+        "        'Kendaraan': 2\n",
+        "    }\n",
+        "])\n",
+        "\n",
+        "pred_dt = decision_tree_model.predict(contoh_mahasiswa)\n",
+        "pred_nb = naive_bayes_model.predict(contoh_mahasiswa)\n",
+        "\n",
+        "hasil_prediksi = contoh_mahasiswa.copy()\n",
+        "hasil_prediksi['Prediksi Decision Tree'] = [label_map[p] for p in pred_dt]\n",
+        "hasil_prediksi['Prediksi Naive Bayes'] = [label_map[p] for p in pred_nb]\n",
+        "\n",
+        "display(hasil_prediksi)\n"
+      ],
+      "id": "xhSEJoi1lYlB"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QAxkMyhulYlB"
+      },
+      "source": [
+        "## 13. Pemeriksaan Indikasi Label-Noise\n",
+        "\n",
+        "Label-noise adalah kondisi ketika label pada data kemungkinan tidak konsisten dengan pola umum data. Bagian ini tidak digunakan untuk menghapus data secara otomatis, tetapi untuk memberi catatan bahwa beberapa baris sebaiknya divalidasi kembali ke pemilik data.\n"
+      ],
+      "id": "QAxkMyhulYlB"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 680
+        },
+        "id": "u9MsaomvlYlB",
+        "outputId": "c6c9deb0-645b-485b-956d-07f32821dee1"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Jumlah baris dengan indikasi label-noise: 19 dari 100 data\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "    Tempat Tinggal Pekerjaan Orang Tua  Penghasilan Orang Tua  \\\n",
+              "0                0                 PNS               10000000   \n",
+              "4                0               Buruh                2000000   \n",
+              "14               0    Ibu Rumah Tangga                3000000   \n",
+              "18               0               Buruh                2000000   \n",
+              "22               1    Ibu Rumah Tangga                5000000   \n",
+              "24               1          Wiraswasta                6000000   \n",
+              "28               1               Buruh                2000000   \n",
+              "34               0               Buruh                2000000   \n",
+              "38               1    Ibu Rumah Tangga                5000000   \n",
+              "44               1               Buruh                2000000   \n",
+              "50               0               Buruh                2000000   \n",
+              "54               1    Ibu Rumah Tangga                5000000   \n",
+              "60               1               Buruh                2000000   \n",
+              "66               0               Buruh                2000000   \n",
+              "70               1    Ibu Rumah Tangga                5000000   \n",
+              "76               1               Buruh                2000000   \n",
+              "82               0               Buruh                2000000   \n",
+              "86               1    Ibu Rumah Tangga                5000000   \n",
+              "92               1               Buruh                2000000   \n",
+              "\n",
+              "    Jumlah Tanggungan Orang Tua  Kendaraan Label Aktual Label Heuristik Teks  \n",
+              "0                             3          1        Layak          Tidak Layak  \n",
+              "4                             2          1  Tidak Layak                Layak  \n",
+              "14                            4          1  Tidak Layak                Layak  \n",
+              "18                            2          1  Tidak Layak                Layak  \n",
+              "22                            3          0  Tidak Layak                Layak  \n",
+              "24                            1          1        Layak          Tidak Layak  \n",
+              "28                            2          1  Tidak Layak                Layak  \n",
+              "34                            2          1  Tidak Layak                Layak  \n",
+              "38                            3          0  Tidak Layak                Layak  \n",
+              "44                            2          1  Tidak Layak                Layak  \n",
+              "50                            2          1  Tidak Layak                Layak  \n",
+              "54                            3          0  Tidak Layak                Layak  \n",
+              "60                            2          1  Tidak Layak                Layak  \n",
+              "66                            2          1  Tidak Layak                Layak  \n",
+              "70                            3          0  Tidak Layak                Layak  \n",
+              "76                            2          1  Tidak Layak                Layak  \n",
+              "82                            2          1  Tidak Layak                Layak  \n",
+              "86                            3          0  Tidak Layak                Layak  \n",
+              "92                            2          1  Tidak Layak                Layak  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-efc5942c-2df3-4f77-81a6-ac1c5858bea0\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Tempat Tinggal</th>\n",
+              "      <th>Pekerjaan Orang Tua</th>\n",
+              "      <th>Penghasilan Orang Tua</th>\n",
+              "      <th>Jumlah Tanggungan Orang Tua</th>\n",
+              "      <th>Kendaraan</th>\n",
+              "      <th>Label Aktual</th>\n",
+              "      <th>Label Heuristik Teks</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>PNS</td>\n",
+              "      <td>10000000</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Layak</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "      <td>Buruh</td>\n",
+              "      <td>2000000</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "      <td>Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>0</td>\n",
+              "      <td>Ibu Rumah Tangga</td>\n",
+              "      <td>3000000</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "      <td>Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>0</td>\n",
+              "      <td>Buruh</td>\n",
+              "      <td>2000000</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "      <td>Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Ibu Rumah Tangga</td>\n",
+              "      <td>5000000</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "      <td>Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Wiraswasta</td>\n",
+              "      <td>6000000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Layak</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>28</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Buruh</td>\n",
+              "      <td>2000000</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "      <td>Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>34</th>\n",
+              "      <td>0</td>\n",
+              "      <td>Buruh</td>\n",
+              "      <td>2000000</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "      <td>Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>38</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Ibu Rumah Tangga</td>\n",
+              "      <td>5000000</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "      <td>Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>44</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Buruh</td>\n",
+              "      <td>2000000</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "      <td>Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50</th>\n",
+              "      <td>0</td>\n",
+              "      <td>Buruh</td>\n",
+              "      <td>2000000</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "      <td>Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>54</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Ibu Rumah Tangga</td>\n",
+              "      <td>5000000</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "      <td>Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>60</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Buruh</td>\n",
+              "      <td>2000000</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "      <td>Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>66</th>\n",
+              "      <td>0</td>\n",
+              "      <td>Buruh</td>\n",
+              "      <td>2000000</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "      <td>Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>70</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Ibu Rumah Tangga</td>\n",
+              "      <td>5000000</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "      <td>Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>76</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Buruh</td>\n",
+              "      <td>2000000</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "      <td>Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>82</th>\n",
+              "      <td>0</td>\n",
+              "      <td>Buruh</td>\n",
+              "      <td>2000000</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "      <td>Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>86</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Ibu Rumah Tangga</td>\n",
+              "      <td>5000000</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "      <td>Layak</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>92</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Buruh</td>\n",
+              "      <td>2000000</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Tidak Layak</td>\n",
+              "      <td>Layak</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-efc5942c-2df3-4f77-81a6-ac1c5858bea0')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-efc5942c-2df3-4f77-81a6-ac1c5858bea0 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-efc5942c-2df3-4f77-81a6-ac1c5858bea0');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"]\",\n  \"rows\": 19,\n  \"fields\": [\n    {\n      \"column\": \"Tempat Tinggal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pekerjaan Orang Tua\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Buruh\",\n          \"Wiraswasta\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Penghasilan Orang Tua\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2169700,\n        \"min\": 2000000,\n        \"max\": 10000000,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2000000,\n          6000000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jumlah Tanggungan Orang Tua\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Kendaraan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label Aktual\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Tidak Layak\",\n          \"Layak\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Label Heuristik Teks\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Layak\",\n          \"Tidak Layak\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "pekerjaan_mampu = {'PNS', 'TNI/POLRI', 'Wiraswasta', 'Guru'}\n",
+        "pekerjaan_kurang_mampu = {'Buruh', 'Petani', 'Nelayan', 'Ibu Rumah Tangga'}\n",
+        "\n",
+        "def prediksi_heuristik(row):\n",
+        "    skor = 0\n",
+        "\n",
+        "    if row['Penghasilan Orang Tua'] <= 4_000_000:\n",
+        "        skor += 2\n",
+        "    elif row['Penghasilan Orang Tua'] <= 6_000_000:\n",
+        "        skor += 1\n",
+        "    else:\n",
+        "        skor -= 2\n",
+        "\n",
+        "    if row['Jumlah Tanggungan Orang Tua'] >= 4:\n",
+        "        skor += 2\n",
+        "    elif row['Jumlah Tanggungan Orang Tua'] <= 2:\n",
+        "        skor -= 1\n",
+        "\n",
+        "    if row['Pekerjaan Orang Tua'] in pekerjaan_kurang_mampu:\n",
+        "        skor += 1\n",
+        "    elif row['Pekerjaan Orang Tua'] in pekerjaan_mampu:\n",
+        "        skor -= 1\n",
+        "\n",
+        "    if row['Kendaraan'] >= 2:\n",
+        "        skor -= 1\n",
+        "\n",
+        "    if row['Tempat Tinggal'] == 1:\n",
+        "        skor += 0.5\n",
+        "\n",
+        "    return 0 if skor >= 1 else 1\n",
+        "\n",
+        "noise_check = df.copy()\n",
+        "noise_check['Label Heuristik'] = noise_check.apply(prediksi_heuristik, axis=1)\n",
+        "noise_check['Indikasi Label Noise'] = noise_check['Label Heuristik'] != noise_check['Kelayakan Keringanan UKT']\n",
+        "\n",
+        "calon_noise = noise_check[noise_check['Indikasi Label Noise']].copy()\n",
+        "calon_noise['Label Aktual'] = calon_noise['Kelayakan Keringanan UKT'].map(label_map)\n",
+        "calon_noise['Label Heuristik Teks'] = calon_noise['Label Heuristik'].map(label_map)\n",
+        "\n",
+        "print(f'Jumlah baris dengan indikasi label-noise: {len(calon_noise)} dari {len(df)} data')\n",
+        "display(calon_noise[\n",
+        "    ['Tempat Tinggal', 'Pekerjaan Orang Tua', 'Penghasilan Orang Tua',\n",
+        "     'Jumlah Tanggungan Orang Tua', 'Kendaraan', 'Label Aktual', 'Label Heuristik Teks']\n",
+        "].head(20))\n"
+      ],
+      "id": "u9MsaomvlYlB"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E0-aYc7xlYlC"
+      },
+      "source": [
+        "## 14. Kesimpulan\n",
+        "\n",
+        "Berdasarkan hasil eksplorasi data, kolom `Kelayakan Keringanan UKT` digunakan sebagai target dengan interpretasi 0 = Layak dan 1 = Tidak Layak. Setelah pembersihan, nilai target yang semula tertulis `1 1` diperbaiki menjadi `1` agar format label konsisten.\n",
+        "\n",
+        "Pola utama pada dataset menunjukkan bahwa mahasiswa dari keluarga dengan penghasilan orang tua lebih tinggi, jumlah tanggungan lebih sedikit, serta pekerjaan orang tua yang relatif stabil atau mampu cenderung masuk kelas Tidak Layak. Sebaliknya, keluarga dengan penghasilan lebih rendah, jumlah tanggungan lebih banyak, dan pekerjaan yang lebih rentan secara ekonomi cenderung masuk kelas Layak. Kolom `Tempat Tinggal` digunakan dengan asumsi 0 = Rumah Sendiri dan 1 = Bukan Rumah Sendiri, tetapi hubungan kolom ini terhadap target tidak sekuat penghasilan, tanggungan, dan pekerjaan orang tua.\n",
+        "\n",
+        "Model Decision Tree sesuai untuk kasus ini karena hasilnya dapat dijelaskan melalui aturan keputusan dan feature importance. Naive Bayes digunakan sebagai model pembanding yang sederhana dan cepat. Model terbaik dapat dipilih dari tabel perbandingan berdasarkan nilai accuracy, precision, recall, dan F1-score, dengan mempertimbangkan bahwa pada kasus bantuan/keringanan biaya, kesalahan memprediksi mahasiswa yang sebenarnya layak perlu diperhatikan secara serius.\n",
+        "\n",
+        "### Catatan Temuan Label-Noise\n",
+        "\n",
+        "Dataset menunjukkan indikasi label-noise. Pertama, terdapat kesalahan format label `1 1` yang telah dibersihkan menjadi `1`. Kedua, ada beberapa baris yang label aktualnya tidak sejalan dengan pola ekonomi yang dominan pada data, misalnya penghasilan rendah dengan pekerjaan kurang mampu tetapi berlabel Tidak Layak, atau penghasilan tinggi dengan tanggungan sedikit tetapi berlabel Layak. Temuan ini tidak otomatis berarti data salah, karena bisa saja ada faktor lain di luar dataset. Namun, sebelum model digunakan untuk pengambilan keputusan nyata, baris-baris tersebut sebaiknya divalidasi kembali kepada pemilik data atau sumber administrasi kampus.\n"
+      ],
+      "id": "E0-aYc7xlYlC"
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.12.6"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/README_STREAMLIT.md b/README_STREAMLIT.md
new file mode 100644
index 0000000..a4dcd96
--- /dev/null
+++ b/README_STREAMLIT.md
@@ -0,0 +1,21 @@
+# Deployment Streamlit UKT
+
+File utama aplikasi:
+
+```bash
+streamlit run streamlit_app.py
+```
+
+Isi aplikasi:
+
+- Ringkasan dataset dan performa model.
+- Pilih data lama dari dataset lalu lihat hasil prediksinya.
+- Input data mahasiswa baru lalu lihat prediksi dari Decision Tree dan Naive Bayes.
+- Simpan input sebagai riwayat prediksi.
+- Tambahkan data baru ke dataset latih jika label aktualnya sudah diketahui.
+- Download dataset terbaru dan riwayat prediksi.
+
+Catatan:
+
+- File `klasifikasi_mhs.csv` harus berada satu folder dengan `streamlit_app.py`.
+- Jika data baru ditambahkan ke dataset latih, aplikasi akan memuat ulang model dari CSV terbaru.