diff --git a/Klasifikasi Tingkat Kemiskinan/Revisi_Klasifikasi_Tingkat_Kemiskinan_di_Indonesia.ipynb b/Klasifikasi Tingkat Kemiskinan/Revisi_Klasifikasi_Tingkat_Kemiskinan_di_Indonesia.ipynb
deleted file mode 100644
index 7fe8a3e..0000000
--- a/Klasifikasi Tingkat Kemiskinan/Revisi_Klasifikasi_Tingkat_Kemiskinan_di_Indonesia.ipynb	
+++ /dev/null
@@ -1,1773 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "code",
-      "source": [
-        "import pandas as pd\n",
-        "import numpy as np\n",
-        "import seaborn as sns\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "# One-Hot Encoding\n",
-        "X_encoded = pd.get_dummies(X, drop_first=True)\n"
-      ],
-      "metadata": {
-        "id": "i4e7z6erwlmV"
-      },
-      "execution_count": 241,
-      "outputs": []
-    },
-    {
-      "source": [
-        "df = pd.read_csv('/content/Klasifikasi Tingkat Kemiskinan di Indonesia.csv', sep=';')\n",
-        "df.head(100)"
-      ],
-      "cell_type": "code",
-      "execution_count": 242,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "            Provinsi            Kab/Kota  \\\n",
-              "0               ACEH            Simeulue   \n",
-              "1               ACEH        Aceh Singkil   \n",
-              "2               ACEH        Aceh Selatan   \n",
-              "3               ACEH       Aceh Tenggara   \n",
-              "4               ACEH          Aceh Timur   \n",
-              "..               ...                 ...   \n",
-              "95             JAMBI               Bungo   \n",
-              "96             JAMBI          Kota Jambi   \n",
-              "97             JAMBI   Kota Sungai Penuh   \n",
-              "98  SUMATERA SELATAN   Ogan Komering Ulu   \n",
-              "99  SUMATERA SELATAN  Ogan Komering Ilir   \n",
-              "\n",
-              "   Persentase Penduduk Miskin (P0) Menurut Kabupaten/Kota (Persen)  \\\n",
-              "0                                               18,98                \n",
-              "1                                               20,36                \n",
-              "2                                               13,18                \n",
-              "3                                               13,41                \n",
-              "4                                               14,45                \n",
-              "..                                                ...                \n",
-              "95                                               6,23                \n",
-              "96                                               9,02                \n",
-              "97                                               3,41                \n",
-              "98                                              12,62                \n",
-              "99                                              14,68                \n",
-              "\n",
-              "   Rata-rata Lama Sekolah Penduduk 15+ (Tahun)  \\\n",
-              "0                                         9,48   \n",
-              "1                                         8,68   \n",
-              "2                                         8,88   \n",
-              "3                                         9,67   \n",
-              "4                                         8,21   \n",
-              "..                                         ...   \n",
-              "95                                        8,28   \n",
-              "96                                        11,2   \n",
-              "97                                       10,33   \n",
-              "98                                        8,71   \n",
-              "99                                        7,05   \n",
-              "\n",
-              "    Pengeluaran per Kapita Disesuaikan (Ribu Rupiah/Orang/Tahun)  \\\n",
-              "0                                              7148.0              \n",
-              "1                                              8776.0              \n",
-              "2                                              8180.0              \n",
-              "3                                              8030.0              \n",
-              "4                                              8577.0              \n",
-              "..                                                ...              \n",
-              "95                                            11670.0              \n",
-              "96                                            12240.0              \n",
-              "97                                            10454.0              \n",
-              "98                                            10040.0              \n",
-              "99                                            10755.0              \n",
-              "\n",
-              "   Indeks Pembangunan Manusia Umur Harapan Hidup (Tahun)  \\\n",
-              "0                       66,41                      65,28   \n",
-              "1                       69,22                      67,43   \n",
-              "2                       67,44                       64,4   \n",
-              "3                       69,44                      68,22   \n",
-              "4                       67,83                      68,74   \n",
-              "..                        ...                        ...   \n",
-              "95                      70,15                      67,83   \n",
-              "96                      79,12                      72,71   \n",
-              "97                       75,7                      72,21   \n",
-              "98                       69,6                      68,24   \n",
-              "99                      67,17                      68,67   \n",
-              "\n",
-              "   Persentase rumah tangga yang memiliki akses terhadap sanitasi layak  \\\n",
-              "0                                               71,56                    \n",
-              "1                                               69,56                    \n",
-              "2                                               62,55                    \n",
-              "3                                               62,71                    \n",
-              "4                                               66,75                    \n",
-              "..                                                ...                    \n",
-              "95                                              77,58                    \n",
-              "96                                              93,22                    \n",
-              "97                                              74,04                    \n",
-              "98                                              82,72                    \n",
-              "99                                              68,09                    \n",
-              "\n",
-              "   Persentase rumah tangga yang memiliki akses terhadap air minum layak  \\\n",
-              "0                                               87,45                     \n",
-              "1                                               78,58                     \n",
-              "2                                               79,65                     \n",
-              "3                                               86,71                     \n",
-              "4                                               83,16                     \n",
-              "..                                                ...                     \n",
-              "95                                              73,63                     \n",
-              "96                                              95,83                     \n",
-              "97                                              90,55                     \n",
-              "98                                              81,78                     \n",
-              "99                                              79,02                     \n",
-              "\n",
-              "   Tingkat Pengangguran Terbuka Tingkat Partisipasi Angkatan Kerja  \\\n",
-              "0                          5,71                             71,15    \n",
-              "1                          8,36                             62,85    \n",
-              "2                          6,46                             60,85    \n",
-              "3                          6,43                             69,62    \n",
-              "4                          7,13                             59,48    \n",
-              "..                          ...                                ...   \n",
-              "95                         5,86                             63,58    \n",
-              "96                        10,66                             63,12    \n",
-              "97                         3,00                             64,92    \n",
-              "98                         4,57                             69,96    \n",
-              "99                         3,01                             69,68    \n",
-              "\n",
-              "    PDRB atas Dasar Harga Konstan menurut Pengeluaran (Rupiah)  \\\n",
-              "0                                           1648096.0            \n",
-              "1                                           1780419.0            \n",
-              "2                                           4345784.0            \n",
-              "3                                           3487157.0            \n",
-              "4                                           8433526.0            \n",
-              "..                                                ...            \n",
-              "95                                         13133523.0            \n",
-              "96                                         19515486.0            \n",
-              "97                                          4768840.0            \n",
-              "98                                         10114558.0            \n",
-              "99                                         20909479.0            \n",
-              "\n",
-              "    Klasifikasi Kemiskinan  \n",
-              "0                      0.0  \n",
-              "1                      1.0  \n",
-              "2                      0.0  \n",
-              "3                      0.0  \n",
-              "4                      0.0  \n",
-              "..                     ...  \n",
-              "95                     0.0  \n",
-              "96                     0.0  \n",
-              "97                     0.0  \n",
-              "98                     0.0  \n",
-              "99                     0.0  \n",
-              "\n",
-              "[100 rows x 13 columns]"
-            ],
-            "text/html": [
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-              "    <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>Provinsi</th>\n",
-              "      <th>Kab/Kota</th>\n",
-              "      <th>Persentase Penduduk Miskin (P0) Menurut Kabupaten/Kota (Persen)</th>\n",
-              "      <th>Rata-rata Lama Sekolah Penduduk 15+ (Tahun)</th>\n",
-              "      <th>Pengeluaran per Kapita Disesuaikan (Ribu Rupiah/Orang/Tahun)</th>\n",
-              "      <th>Indeks Pembangunan Manusia</th>\n",
-              "      <th>Umur Harapan Hidup (Tahun)</th>\n",
-              "      <th>Persentase rumah tangga yang memiliki akses terhadap sanitasi layak</th>\n",
-              "      <th>Persentase rumah tangga yang memiliki akses terhadap air minum layak</th>\n",
-              "      <th>Tingkat Pengangguran Terbuka</th>\n",
-              "      <th>Tingkat Partisipasi Angkatan Kerja</th>\n",
-              "      <th>PDRB atas Dasar Harga Konstan menurut Pengeluaran (Rupiah)</th>\n",
-              "      <th>Klasifikasi Kemiskinan</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>ACEH</td>\n",
-              "      <td>Simeulue</td>\n",
-              "      <td>18,98</td>\n",
-              "      <td>9,48</td>\n",
-              "      <td>7148.0</td>\n",
-              "      <td>66,41</td>\n",
-              "      <td>65,28</td>\n",
-              "      <td>71,56</td>\n",
-              "      <td>87,45</td>\n",
-              "      <td>5,71</td>\n",
-              "      <td>71,15</td>\n",
-              "      <td>1648096.0</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>ACEH</td>\n",
-              "      <td>Aceh Singkil</td>\n",
-              "      <td>20,36</td>\n",
-              "      <td>8,68</td>\n",
-              "      <td>8776.0</td>\n",
-              "      <td>69,22</td>\n",
-              "      <td>67,43</td>\n",
-              "      <td>69,56</td>\n",
-              "      <td>78,58</td>\n",
-              "      <td>8,36</td>\n",
-              "      <td>62,85</td>\n",
-              "      <td>1780419.0</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>ACEH</td>\n",
-              "      <td>Aceh Selatan</td>\n",
-              "      <td>13,18</td>\n",
-              "      <td>8,88</td>\n",
-              "      <td>8180.0</td>\n",
-              "      <td>67,44</td>\n",
-              "      <td>64,4</td>\n",
-              "      <td>62,55</td>\n",
-              "      <td>79,65</td>\n",
-              "      <td>6,46</td>\n",
-              "      <td>60,85</td>\n",
-              "      <td>4345784.0</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>ACEH</td>\n",
-              "      <td>Aceh Tenggara</td>\n",
-              "      <td>13,41</td>\n",
-              "      <td>9,67</td>\n",
-              "      <td>8030.0</td>\n",
-              "      <td>69,44</td>\n",
-              "      <td>68,22</td>\n",
-              "      <td>62,71</td>\n",
-              "      <td>86,71</td>\n",
-              "      <td>6,43</td>\n",
-              "      <td>69,62</td>\n",
-              "      <td>3487157.0</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>ACEH</td>\n",
-              "      <td>Aceh Timur</td>\n",
-              "      <td>14,45</td>\n",
-              "      <td>8,21</td>\n",
-              "      <td>8577.0</td>\n",
-              "      <td>67,83</td>\n",
-              "      <td>68,74</td>\n",
-              "      <td>66,75</td>\n",
-              "      <td>83,16</td>\n",
-              "      <td>7,13</td>\n",
-              "      <td>59,48</td>\n",
-              "      <td>8433526.0</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>95</th>\n",
-              "      <td>JAMBI</td>\n",
-              "      <td>Bungo</td>\n",
-              "      <td>6,23</td>\n",
-              "      <td>8,28</td>\n",
-              "      <td>11670.0</td>\n",
-              "      <td>70,15</td>\n",
-              "      <td>67,83</td>\n",
-              "      <td>77,58</td>\n",
-              "      <td>73,63</td>\n",
-              "      <td>5,86</td>\n",
-              "      <td>63,58</td>\n",
-              "      <td>13133523.0</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>96</th>\n",
-              "      <td>JAMBI</td>\n",
-              "      <td>Kota Jambi</td>\n",
-              "      <td>9,02</td>\n",
-              "      <td>11,2</td>\n",
-              "      <td>12240.0</td>\n",
-              "      <td>79,12</td>\n",
-              "      <td>72,71</td>\n",
-              "      <td>93,22</td>\n",
-              "      <td>95,83</td>\n",
-              "      <td>10,66</td>\n",
-              "      <td>63,12</td>\n",
-              "      <td>19515486.0</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>97</th>\n",
-              "      <td>JAMBI</td>\n",
-              "      <td>Kota Sungai Penuh</td>\n",
-              "      <td>3,41</td>\n",
-              "      <td>10,33</td>\n",
-              "      <td>10454.0</td>\n",
-              "      <td>75,7</td>\n",
-              "      <td>72,21</td>\n",
-              "      <td>74,04</td>\n",
-              "      <td>90,55</td>\n",
-              "      <td>3,00</td>\n",
-              "      <td>64,92</td>\n",
-              "      <td>4768840.0</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>98</th>\n",
-              "      <td>SUMATERA SELATAN</td>\n",
-              "      <td>Ogan Komering Ulu</td>\n",
-              "      <td>12,62</td>\n",
-              "      <td>8,71</td>\n",
-              "      <td>10040.0</td>\n",
-              "      <td>69,6</td>\n",
-              "      <td>68,24</td>\n",
-              "      <td>82,72</td>\n",
-              "      <td>81,78</td>\n",
-              "      <td>4,57</td>\n",
-              "      <td>69,96</td>\n",
-              "      <td>10114558.0</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>99</th>\n",
-              "      <td>SUMATERA SELATAN</td>\n",
-              "      <td>Ogan Komering Ilir</td>\n",
-              "      <td>14,68</td>\n",
-              "      <td>7,05</td>\n",
-              "      <td>10755.0</td>\n",
-              "      <td>67,17</td>\n",
-              "      <td>68,67</td>\n",
-              "      <td>68,09</td>\n",
-              "      <td>79,02</td>\n",
-              "      <td>3,01</td>\n",
-              "      <td>69,68</td>\n",
-              "      <td>20909479.0</td>\n",
-              "      <td>0.0</td>\n",
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-              "     width=\"24px\">\n",
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-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "      </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "      <script>\n",
-              "        async function quickchart(key) {\n",
-              "          const quickchartButtonEl =\n",
-              "            document.querySelector('#' + key + ' button');\n",
-              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "          try {\n",
-              "            const charts = await google.colab.kernel.invokeFunction(\n",
-              "                'suggestCharts', [key], {});\n",
-              "          } catch (error) {\n",
-              "            console.error('Error during call to suggestCharts:', error);\n",
-              "          }\n",
-              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "        }\n",
-              "        (() => {\n",
-              "          let quickchartButtonEl =\n",
-              "            document.querySelector('#df-caba88ae-daeb-4415-9876-0df7cee1305e button');\n",
-              "          quickchartButtonEl.style.display =\n",
-              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "        })();\n",
-              "      </script>\n",
-              "    </div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df",
-              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 999,\n  \"fields\": [\n    {\n      \"column\": \"Provinsi\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 34,\n        \"samples\": [\n          \"BANTEN\",\n          \"KALIMANTAN BARAT\",\n          \"SULAWESI TENGGARA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Kab/Kota\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 514,\n        \"samples\": [\n          \"Manggarai\",\n          \"Yahukimo\",\n          \"Gorontalo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Persentase Penduduk Miskin (P0) Menurut Kabupaten/Kota (Persen)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 452,\n        \"samples\": [\n          \"5,91\",\n          \"8,3\",\n          \"12,85\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rata-rata Lama Sekolah Penduduk 15+ (Tahun)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 377,\n        \"samples\": [\n          \"10,91\",\n          \"7,12\",\n          \"6,02\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pengeluaran per Kapita Disesuaikan (Ribu Rupiah/Orang/Tahun)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2717.144185738409,\n        \"min\": 3976.0,\n        \"max\": 23888.0,\n        \"num_unique_values\": 498,\n        \"samples\": [\n          5708.0,\n          13317.0,\n          9410.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Indeks Pembangunan Manusia\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 452,\n        \"samples\": [\n          \"65,87\",\n          \"70,83\",\n          \"80,59\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Umur Harapan Hidup (Tahun)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 411,\n        \"samples\": [\n          \"73,8\",\n          \"71,58\",\n          \"70,38\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Persentase rumah tangga yang memiliki akses terhadap sanitasi layak\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 487,\n        \"samples\": [\n          \"89,49\",\n          \"91,56\",\n          \"90,56\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Persentase rumah tangga yang memiliki akses terhadap air minum layak\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 465,\n        \"samples\": [\n          \"74,11\",\n          \"85,78\",\n          \"91,43\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tingkat Pengangguran Terbuka\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 373,\n        \"samples\": [\n          \"4,56\",\n          \"1,95\",\n          \"3,47\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tingkat Partisipasi Angkatan Kerja\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 448,\n        \"samples\": [\n          \" 77,57 \",\n          \" 70,16 \",\n          \" 70,13 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PDRB atas Dasar Harga Konstan menurut Pengeluaran (Rupiah)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 47904920.44381927,\n        \"min\": 147485.0,\n        \"max\": 460081046.0,\n        \"num_unique_values\": 514,\n        \"samples\": [\n          3025880.0,\n          1556231.0,\n          9082312.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Klasifikasi Kemiskinan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3260054197106571,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 242
-        }
-      ],
-      "metadata": {
-        "cellView": "form",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 774
-        },
-        "id": "jpmStKaasKmD",
-        "outputId": "c7d076a5-b771-421a-9308-d94f3d61bd05"
-      }
-    },
-    {
-      "source": [
-        "df_subset = df[[\"Provinsi\", \"Klasifikasi Kemiskinan\"]]\n",
-        "df_subset.head(100)"
-      ],
-      "cell_type": "code",
-      "execution_count": 243,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "            Provinsi  Klasifikasi Kemiskinan\n",
-              "0               ACEH                     0.0\n",
-              "1               ACEH                     1.0\n",
-              "2               ACEH                     0.0\n",
-              "3               ACEH                     0.0\n",
-              "4               ACEH                     0.0\n",
-              "..               ...                     ...\n",
-              "95             JAMBI                     0.0\n",
-              "96             JAMBI                     0.0\n",
-              "97             JAMBI                     0.0\n",
-              "98  SUMATERA SELATAN                     0.0\n",
-              "99  SUMATERA SELATAN                     0.0\n",
-              "\n",
-              "[100 rows x 2 columns]"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-ebc80c7d-c73d-42b9-847d-11730a6527f4\" 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>Provinsi</th>\n",
-              "      <th>Klasifikasi Kemiskinan</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>ACEH</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>ACEH</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>ACEH</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>ACEH</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>ACEH</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>95</th>\n",
-              "      <td>JAMBI</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>96</th>\n",
-              "      <td>JAMBI</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>97</th>\n",
-              "      <td>JAMBI</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>98</th>\n",
-              "      <td>SUMATERA SELATAN</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>99</th>\n",
-              "      <td>SUMATERA SELATAN</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>100 rows × 2 columns</p>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ebc80c7d-c73d-42b9-847d-11730a6527f4')\"\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-ebc80c7d-c73d-42b9-847d-11730a6527f4 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-ebc80c7d-c73d-42b9-847d-11730a6527f4');\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=\"df-16b13a1f-5473-4437-83e1-6bbd9a8f6a8c\">\n",
-              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-16b13a1f-5473-4437-83e1-6bbd9a8f6a8c')\"\n",
-              "                title=\"Suggest charts\"\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",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "      </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "      <script>\n",
-              "        async function quickchart(key) {\n",
-              "          const quickchartButtonEl =\n",
-              "            document.querySelector('#' + key + ' button');\n",
-              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "          try {\n",
-              "            const charts = await google.colab.kernel.invokeFunction(\n",
-              "                'suggestCharts', [key], {});\n",
-              "          } catch (error) {\n",
-              "            console.error('Error during call to suggestCharts:', error);\n",
-              "          }\n",
-              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "        }\n",
-              "        (() => {\n",
-              "          let quickchartButtonEl =\n",
-              "            document.querySelector('#df-16b13a1f-5473-4437-83e1-6bbd9a8f6a8c button');\n",
-              "          quickchartButtonEl.style.display =\n",
-              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "        })();\n",
-              "      </script>\n",
-              "    </div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df_subset",
-              "summary": "{\n  \"name\": \"df_subset\",\n  \"rows\": 999,\n  \"fields\": [\n    {\n      \"column\": \"Provinsi\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 34,\n        \"samples\": [\n          \"BANTEN\",\n          \"KALIMANTAN BARAT\",\n          \"SULAWESI TENGGARA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Klasifikasi Kemiskinan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3260054197106571,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 243
-        }
-      ],
-      "metadata": {
-        "cellView": "form",
-        "id": "0Np7uHb3s1vm",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 424
-        },
-        "outputId": "d6f28af5-16e3-4bbe-934a-acc7e27fef6d"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df_subset = df[[\"Provinsi\", \"Klasifikasi Kemiskinan\"]]\n",
-        "df_subset = df_subset.dropna()\n",
-        "\n",
-        "plt.figure(figsize=(12, 4))\n",
-        "sns.lineplot(data=df_subset, x=\"Provinsi\", y=\"Klasifikasi Kemiskinan\", marker=\"o\")\n",
-        "\n",
-        "plt.xticks(rotation=90)\n",
-        "plt.title(\"Line Plot Klasifikasi Kemiskinan di setiap Provinsi\")\n",
-        "plt.xlabel(\"Provinsi\")\n",
-        "plt.ylabel(\"Klasifikasi Kemiskinan\")\n",
-        "plt.tight_layout()\n",
-        "plt.show()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 398
-        },
-        "id": "9cbPJC3aKczY",
-        "outputId": "0ca834f4-13c0-49cf-de11-c16566bc1076"
-      },
-      "execution_count": 244,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 1200x400 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import pandas as pd\n",
-        "import numpy as np\n",
-        "import seaborn as sns\n",
-        "import matplotlib.pyplot as plt\n",
-        "from sklearn.model_selection import train_test_split, cross_val_score\n",
-        "from sklearn.preprocessing import LabelEncoder\n",
-        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
-        "from sklearn.ensemble import RandomForestClassifier\n",
-        "from sklearn.linear_model import LogisticRegression\n",
-        "from sklearn.impute import SimpleImputer\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "LIvFHTtMw5mI"
-      },
-      "execution_count": 245,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "y = pd.to_numeric(df[\"Klasifikasi Kemiskinan\"], errors='coerce')\n",
-        "y = y.fillna(0).astype(int)\n",
-        "\n",
-        "X = df.drop(\"Klasifikasi Kemiskinan\", axis=1)"
-      ],
-      "metadata": {
-        "id": "LPcA1dxyxERN"
-      },
-      "execution_count": 246,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "X = X.apply(lambda s: s.astype(str).str.replace(\",\", \".\").str.replace(\" \", \"\", regex=True))\n",
-        "\n",
-        "for col in X.columns:\n",
-        "    X[col] = pd.to_numeric(X[col], errors='coerce')\n",
-        "\n",
-        "label_enc = LabelEncoder()\n",
-        "X[\"Provinsi\"] = label_enc.fit_transform(X[\"Provinsi\"].astype(str))\n",
-        "X[\"Kab/Kota\"] = label_enc.fit_transform(X[\"Kab/Kota\"].astype(str))\n",
-        "\n",
-        "imputer = SimpleImputer(strategy='mean')\n",
-        "X[X.columns] = imputer.fit_transform(X)"
-      ],
-      "metadata": {
-        "id": "g_oElrw_xSFf"
-      },
-      "execution_count": 247,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "X_train, X_test, y_train, y_test = train_test_split(\n",
-        "    X, y, test_size=0.2, random_state=42\n",
-        ")\n"
-      ],
-      "metadata": {
-        "id": "QubEOuCOxVE7"
-      },
-      "execution_count": 248,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "rf = RandomForestClassifier(n_estimators=200, random_state=42)\n",
-        "rf.fit(X_train, y_train)\n",
-        "\n",
-        "pred_rf = rf.predict(X_test)\n",
-        "\n",
-        "print(\"=== Random Forest Accuracy ===\")\n",
-        "print(accuracy_score(y_test, pred_rf))\n",
-        "print(\"\\n=== Classification Report ===\")\n",
-        "print(classification_report(y_test, pred_rf))\n",
-        "print(\"\\n=== Confusion Matrix ===\")\n",
-        "print(confusion_matrix(y_test, pred_rf))\n",
-        "\n",
-        "cv_rf = cross_val_score(rf, X, y, cv=5)\n",
-        "print(\"\\n=== Cross Validation (Random Forest) ===\")\n",
-        "print(cv_rf)\n",
-        "print(\"Mean CV Score:\", cv_rf.mean())\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "aEaCyi8XxXql",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "0c297472-00b2-4e18-f3da-b29a050a8a9e"
-      },
-      "execution_count": 249,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "=== Random Forest Accuracy ===\n",
-            "0.99\n",
-            "\n",
-            "=== Classification Report ===\n",
-            "              precision    recall  f1-score   support\n",
-            "\n",
-            "           0       0.99      1.00      0.99       190\n",
-            "           1       1.00      0.80      0.89        10\n",
-            "\n",
-            "    accuracy                           0.99       200\n",
-            "   macro avg       0.99      0.90      0.94       200\n",
-            "weighted avg       0.99      0.99      0.99       200\n",
-            "\n",
-            "\n",
-            "=== Confusion Matrix ===\n",
-            "[[190   0]\n",
-            " [  2   8]]\n",
-            "\n",
-            "=== Cross Validation (Random Forest) ===\n",
-            "[0.99 1.   0.97 0.99 1.  ]\n",
-            "Mean CV Score: 0.99\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "import numpy as np\n"
-      ],
-      "metadata": {
-        "id": "l_Bms2snyjks"
-      },
-      "execution_count": 250,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "accuracy = 0.99\n",
-        "\n",
-        "conf_matrix = np.array([\n",
-        "    [190, 0],\n",
-        "    [2,   8]\n",
-        "])"
-      ],
-      "metadata": {
-        "id": "isQJoC9Tyq5G"
-      },
-      "execution_count": 251,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "classes = [\"0\", \"1\"]\n",
-        "precision = [0.99, 1.00]\n",
-        "recall = [1.00, 0.80]\n",
-        "f1 = [0.99, 0.89]\n",
-        "\n",
-        "cv_scores = np.array([0.99, 1.00, 0.97, 0.99, 1.00])"
-      ],
-      "metadata": {
-        "id": "ZmNBm0wyzKA8"
-      },
-      "execution_count": 252,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "plt.figure()\n",
-        "plt.bar([\"Accuracy\"], [accuracy])\n",
-        "plt.title(\"Random Forest Accuracy\")\n",
-        "plt.ylabel(\"Score\")\n",
-        "plt.ylim(0, 1)\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 452
-        },
-        "id": "kTf7LP5my2ja",
-        "outputId": "5dd533a5-59e8-40e1-a666-885fa6511458"
-      },
-      "execution_count": 253,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "conf_matrix = np.array([[190, 0],\n",
-        "                        [2, 8]])\n",
-        "\n",
-        "plt.figure(figsize=(6,4))\n",
-        "plt.imshow(conf_matrix)\n",
-        "plt.title(\"Confusion Matrix Random Forest\")\n",
-        "plt.colorbar()\n",
-        "plt.xticks([0,1], [\"Pred 0\", \"Pred 1\"])\n",
-        "plt.yticks([0,1], [\"Actual 0\", \"Actual 1\"])\n",
-        "plt.show()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 391
-        },
-        "id": "mV2_uYR1y5zj",
-        "outputId": "d70dbefb-0f3e-4d2b-b57d-50ee0959e3b1"
-      },
-      "execution_count": 254,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 600x400 with 2 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "x = np.arange(len(classes))\n",
-        "width = 0.25\n",
-        "\n",
-        "plt.figure()\n",
-        "plt.bar(x - width, precision, width, label=\"Precision\")\n",
-        "plt.bar(x, recall, width, label=\"Recall\")\n",
-        "plt.bar(x + width, f1, width, label=\"F1-Score\")\n",
-        "\n",
-        "plt.xticks(x, classes)\n",
-        "plt.ylabel(\"Score\")\n",
-        "plt.title(\"Classification Metrics per Class\")\n",
-        "plt.legend()\n",
-        "plt.ylim(0, 1)\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 452
-        },
-        "id": "OgLQduE6y85d",
-        "outputId": "28b910bf-caed-497a-cf1e-8e22ef8bf04f"
-      },
-      "execution_count": 255,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "cv_scores = [0.99, 1.00, 0.97, 0.99, 1.00]\n",
-        "\n",
-        "plt.figure(figsize=(7,4))\n",
-        "plt.plot(cv_scores, marker='o')\n",
-        "plt.title(\"Cross Validation Scores (Random Forest)\")\n",
-        "plt.xlabel(\"Fold\")\n",
-        "plt.ylabel(\"Score\")\n",
-        "plt.ylim(0,1)\n",
-        "plt.grid(True)\n",
-        "plt.show()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 410
-        },
-        "id": "EhayQ2eQyyIL",
-        "outputId": "d603d162-79b2-43a2-9ea7-883d79872f7d"
-      },
-      "execution_count": 256,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 700x400 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "logreg = LogisticRegression(max_iter=2000)\n",
-        "logreg.fit(X_train, y_train)\n",
-        "\n",
-        "pred_lr = logreg.predict(X_test)\n",
-        "\n",
-        "print(\"\\n=== Logistic Regression Accuracy ===\")\n",
-        "print(accuracy_score(y_test, pred_lr))\n",
-        "print(\"\\n=== Classification Report ===\")\n",
-        "print(classification_report(y_test, pred_lr))\n",
-        "print(\"\\n=== Confusion Matrix ===\")\n",
-        "print(confusion_matrix(y_test, pred_lr))\n",
-        "\n",
-        "cv_lr = cross_val_score(logreg, X, y, cv=5)\n",
-        "print(\"\\n=== Cross Validation (Logistic Regression) ===\")\n",
-        "print(cv_lr)\n",
-        "print(\"Mean CV Score:\", cv_lr.mean())\n",
-        "\n",
-        "\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "8pScuIEBxcuQ",
-        "outputId": "5b2f8e01-fd44-45c4-993a-a1e2ac452270"
-      },
-      "execution_count": 257,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\n",
-            "=== Logistic Regression Accuracy ===\n",
-            "0.985\n",
-            "\n",
-            "=== Classification Report ===\n",
-            "              precision    recall  f1-score   support\n",
-            "\n",
-            "           0       0.98      1.00      0.99       190\n",
-            "           1       1.00      0.70      0.82        10\n",
-            "\n",
-            "    accuracy                           0.98       200\n",
-            "   macro avg       0.99      0.85      0.91       200\n",
-            "weighted avg       0.99      0.98      0.98       200\n",
-            "\n",
-            "\n",
-            "=== Confusion Matrix ===\n",
-            "[[190   0]\n",
-            " [  3   7]]\n",
-            "\n",
-            "=== Cross Validation (Logistic Regression) ===\n",
-            "[0.975 0.98  0.965 0.99  1.   ]\n",
-            "Mean CV Score: 0.982\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "plt.figure()\n",
-        "plt.bar([\"Accuracy\"], [accuracy])\n",
-        "plt.title(\"Logistic Regression Accuracy\")\n",
-        "plt.ylabel(\"Score\")\n",
-        "plt.ylim(0, 1)\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "id": "dLeeMyia2oWm",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 452
-        },
-        "outputId": "28b7a674-314d-4508-fc5f-a39ae49a3e2f"
-      },
-      "execution_count": 258,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "plt.figure()\n",
-        "plt.imshow(conf_matrix)\n",
-        "plt.title(\"Confusion Matrix Logistic Regression\")\n",
-        "plt.colorbar()\n",
-        "plt.xticks([0,1], [\"Pred 0\", \"Pred 1\"])\n",
-        "plt.yticks([0,1], [\"Actual 0\", \"Actual 1\"])\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "id": "uqY9L5R82sCw",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 452
-        },
-        "outputId": "5cf3860f-533e-465f-f627-e03d4f812796"
-      },
-      "execution_count": 259,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 2 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "x = np.arange(len(classes))\n",
-        "width = 0.25\n",
-        "\n",
-        "plt.figure()\n",
-        "plt.bar(x - width, precision, width, label=\"Precision\")\n",
-        "plt.bar(x, recall, width, label=\"Recall\")\n",
-        "plt.bar(x + width, f1, width, label=\"F1-Score\")\n",
-        "plt.xticks(x, classes)\n",
-        "plt.ylabel(\"Score\")\n",
-        "plt.title(\"Classification Metrics per Class (Logistic Regression)\")\n",
-        "plt.legend()\n",
-        "plt.ylim(0, 1)\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "id": "5XsjRubu21tt",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 452
-        },
-        "outputId": "ea06da93-3ca1-4c85-9de7-d9c02fef861d"
-      },
-      "execution_count": 260,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "plt.figure()\n",
-        "plt.plot(cv_scores, marker='o')\n",
-        "plt.title(\"Cross Validation Scores (Logistic Regression)\")\n",
-        "plt.xlabel(\"Fold\")\n",
-        "plt.ylabel(\"Score\")\n",
-        "plt.ylim(0, 1)\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "id": "sF-dgugW28Dt",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 472
-        },
-        "outputId": "6687fe34-170c-4e89-8068-85bd11e99ce9"
-      },
-      "execution_count": 261,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import pandas as pd\n",
-        "import numpy as np\n",
-        "\n",
-        "from sklearn.model_selection import train_test_split, cross_val_score, StratifiedKFold\n",
-        "from sklearn.tree import DecisionTreeClassifier\n",
-        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
-        "\n",
-        "import matplotlib.pyplot as plt\n",
-        "import seaborn as sns"
-      ],
-      "metadata": {
-        "id": "ddHu3Ditf64F"
-      },
-      "execution_count": 262,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "y = pd.to_numeric(df[\"Klasifikasi Kemiskinan\"], errors='coerce')\n",
-        "y = y.fillna(0).astype(int)\n",
-        "\n",
-        "X = df.drop(\"Klasifikasi Kemiskinan\", axis=1)"
-      ],
-      "metadata": {
-        "id": "mJFidT4pkw_h"
-      },
-      "execution_count": 263,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from sklearn.model_selection import train_test_split\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",
-        ")"
-      ],
-      "metadata": {
-        "id": "LxqkKvx8lDJh"
-      },
-      "execution_count": 264,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "import numpy as np\n",
-        "from sklearn.metrics import confusion_matrix\n",
-        "\n",
-        "# Jika belum ada y_test dan y_pred\n",
-        "cm = confusion_matrix(y_test, y_pred)\n",
-        "\n",
-        "plt.figure()\n",
-        "plt.imshow(cm)\n",
-        "plt.title(\"Confusion Matrix - Decision Tree\")\n",
-        "plt.xlabel(\"Predicted Label\")\n",
-        "plt.ylabel(\"True Label\")\n",
-        "plt.xticks([0, 1], ['Kelas 0', 'Kelas 1'])\n",
-        "plt.yticks([0, 1], ['Kelas 0', 'Kelas 1'])\n",
-        "\n",
-        "# Menampilkan nilai di dalam matriks\n",
-        "for i in range(cm.shape[0]):\n",
-        "    for j in range(cm.shape[1]):\n",
-        "        plt.text(j, i, cm[i, j], ha='center', va='center')\n",
-        "\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 472
-        },
-        "id": "MrObTJ_GlwwV",
-        "outputId": "2ee51d51-69fb-4a86-8cba-bd357a482693"
-      },
-      "execution_count": 265,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "accuracy = accuracy_score(y_test, y_pred)\n",
-        "\n",
-        "plt.figure()\n",
-        "plt.bar(['Decision Tree'], [accuracy])\n",
-        "plt.title('Accuracy Model Decision Tree')\n",
-        "plt.ylabel('Accuracy')\n",
-        "plt.ylim(0, 1)\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 452
-        },
-        "id": "r6NBUXRJmHQJ",
-        "outputId": "54fc77ca-5230-4f94-d1ff-1dba931b08c8"
-      },
-      "execution_count": 266,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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b3/6mxMREbdmyRX369FFpaak++eQT3X///Ro+fLgCAgI0evRovfLKK7JYLIqMjNRHH33k1HyQ9u3bKzIyUtOnT9fx48fl7++v999/v8o5ES+//LJ69+6trl27asqUKWrVqpWOHDmiNWvWaNeuXQ594+PjNWrUKEnS3Llzq38yf2X48OEaPnz4RftERUUpISFBr7/+ugoLC9WvXz9t2bJFb7/9tkaMGGGf4B0UFKTp06crNTVVQ4cO1eDBg7Vz5059/PHHlcLXY489ptWrV2vo0KH2pculpaXau3evVqxYoSNHjtQosO3YsUPvvvuubDabCgsLtXXrVvuE4Hfeecdhvkt1a+jfv7/uvvtuvfzyyzp48KAGDRokm82mzz77TP3799fUqVOrrKVDhw666aab1K1bNzVp0kTbtm3TihUrLtjfmXPtLv7+/nrttdd09913q2vXrho7dqyCgoKUk5OjNWvW6MYbb7T/wbBw4UL17t1bHTt21OTJk9W6dWvl5eUpKytL33//vXbv3u3WY0Ed5p5FWsDlNWzYMMPHx8coLS29YJ/x48cbXl5eRkFBgWEYhvGf//zHmDp1qhEaGmp4e3sb11xzjZGQkGD/3DB+WaL9xBNPGK1atTK8vLyMkJAQY9SoUUZ2dra9z4kTJ4yRI0ca9evXNxo3bmzce++9xr59+6pcCu7n51dlbfv37zdiY2ONBg0aGIGBgcbkyZON3bt3VxrDMAxj3759xu233240atTI8PHxMdq1a2fMmjWr0phlZWVG48aNjYCAAOP06dPVOY0OS8Ev5tdLwQ3DMM6cOWPMmTPHfq7CwsKMpKQkh2W+hmEYFRUVxpw5c4zmzZsbvr6+xk033WTs27ev0jJpw/hlWXFSUpJx7bXXGt7e3kZgYKBxww03GC+88IJRXl5u7ycnloKfe9WrV89o0qSJER0dbSQlJVVaXu9sDWfPnjX+/Oc/G+3btze8vb2NoKAg49ZbbzW2b99u7/PrY3z66aeNnj17Go0aNTJ8fX2N9u3bG3/6058cxv31UnBnznV4eHiVS8379etn9OvX76Ln63wXWwq+devWKrfZuHGjERcXZwQEBBg+Pj5GZGSkMX78eGPbtm0O/bKzs434+HgjJCTE8PLyMkJDQ42hQ4caK1asqHZ9uPpYDMNFs8YAXFHOnj2rFi1aaNiwYXrrrbfcXQ4AuAxzboCr1KpVq3TixAmHScoAYAZcuQGuMl9++aX27NmjuXPnKjAwUDt27HB3SQDgUly5Aa4yr732mu677z41a9bMJV+QCAB1jVvDzebNmzVs2DC1aNFCFotFq1atuuQ2mzZtUteuXe3fvnv+tyoDuLSlS5fq7Nmz2rZt2yWfZgwAVyK3hpvS0lJFRUVV+VC1qhw+fFhDhgxR//79tWvXLj388MOaNGlSlc+YAAAAV6c6M+fGYrHogw8+0IgRIy7Y5/HHH9eaNWscnsA6duxYFRYWat26dZehSgAAUNddUQ/xy8rKqvQY7ri4uCq/MficsrIyh6d52mw2nTx5Uk2bNnXp99wAAIDaYxiGSkpK1KJFi0rfwfdrV1S4yc3NVXBwsENbcHCwiouLdfr06Upf0CZJqampVT7yHAAAXHmOHTuma6655qJ9rqhwUxNJSUlKTEy0vy8qKlLLli117Ngx+zf7AgCAuq24uFhhYWFq2LDhJfteUeEmJCREeXl5Dm15eXny9/ev8qqNJFmtVlmt1krt/v7+hBsAAK4w1ZlSckWFm5iYGK1du9ahbf369YqJiXFTRZVFzFjj7hIAAHCrI8+699vn3boU/NSpU9q1a5f924oPHz6sXbt2KScnR9Ivt5TOfzT8H/7wBx06dEh//OMf9fXXX+vVV1/V3//+dz3yyCPuKB8AANRBbg0327ZtU5cuXdSlSxdJUmJiorp06aLk5GRJ0o8//mgPOpLUqlUrrVmzRuvXr1dUVJTmzZunN998U3FxcW6pHwAA1D115jk3l0txcbECAgJUVFRUK3NuuC0FALja1cZtKWd+f/PdUgAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFTcHm4WLlyoiIgI+fj4KDo6Wlu2bLlo//nz56tdu3by9fVVWFiYHnnkEf3888+XqVoAAFDXuTXcZGRkKDExUSkpKdqxY4eioqIUFxen/Pz8KvsvW7ZMM2bMUEpKig4cOKC33npLGRkZmjlz5mWuHAAA1FVuDTcvvviiJk+erAkTJqhDhw5atGiR6tevr8WLF1fZ/4svvtCNN96oO++8UxERERo4cKDGjRt3yas9AADg6uG2cFNeXq7t27crNjb2f8V4eCg2NlZZWVlVbnPDDTdo+/bt9jBz6NAhrV27VoMHD77gfsrKylRcXOzwAgAA5lXPXTsuKChQRUWFgoODHdqDg4P19ddfV7nNnXfeqYKCAvXu3VuGYejs2bP6wx/+cNHbUqmpqZozZ45LawcAAHWX2ycUO2PTpk165pln9Oqrr2rHjh1auXKl1qxZo7lz515wm6SkJBUVFdlfx44du4wVAwCAy81tV24CAwPl6empvLw8h/a8vDyFhIRUuc2sWbN09913a9KkSZKkjh07qrS0VFOmTNETTzwhD4/KWc1qtcpqtbr+AAAAQJ3ktis33t7e6tatmzIzM+1tNptNmZmZiomJqXKbn376qVKA8fT0lCQZhlF7xQIAgCuG267cSFJiYqISEhLUvXt39ezZU/Pnz1dpaakmTJggSYqPj1doaKhSU1MlScOGDdOLL76oLl26KDo6Wt99951mzZqlYcOG2UMOAAC4urk13IwZM0YnTpxQcnKycnNz1blzZ61bt84+yTgnJ8fhSs2TTz4pi8WiJ598UsePH1dQUJCGDRumP/3pT+46BAAAUMdYjKvsfk5xcbECAgJUVFQkf39/l48fMWONy8cEAOBKcuTZIS4f05nf31fUaikAAIBLIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTIdwAAABTcXu4WbhwoSIiIuTj46Po6Ght2bLlov0LCwv1wAMPqHnz5rJarWrbtq3Wrl17maoFAAB1XT137jwjI0OJiYlatGiRoqOjNX/+fMXFxembb75Rs2bNKvUvLy/XgAED1KxZM61YsUKhoaE6evSoGjVqdPmLBwAAdZJbw82LL76oyZMna8KECZKkRYsWac2aNVq8eLFmzJhRqf/ixYt18uRJffHFF/Ly8pIkRUREXM6SAQBAHee221Ll5eXavn27YmNj/1eMh4diY2OVlZVV5TarV69WTEyMHnjgAQUHB+v666/XM888o4qKigvup6ysTMXFxQ4vAABgXm4LNwUFBaqoqFBwcLBDe3BwsHJzc6vc5tChQ1qxYoUqKiq0du1azZo1S/PmzdPTTz99wf2kpqYqICDA/goLC3PpcQAAgLrF7ROKnWGz2dSsWTO9/vrr6tatm8aMGaMnnnhCixYtuuA2SUlJKioqsr+OHTt2GSsGAACXm9vm3AQGBsrT01N5eXkO7Xl5eQoJCalym+bNm8vLy0uenp72tuuuu065ubkqLy+Xt7d3pW2sVqusVqtriwcAAHWW267ceHt7q1u3bsrMzLS32Ww2ZWZmKiYmpsptbrzxRn333Xey2Wz2tm+//VbNmzevMtgAAICrj1tvSyUmJuqNN97Q22+/rQMHDui+++5TaWmpffVUfHy8kpKS7P3vu+8+nTx5UtOmTdO3336rNWvW6JlnntEDDzzgrkMAAAB1jFuXgo8ZM0YnTpxQcnKycnNz1blzZ61bt84+yTgnJ0ceHv/LX2FhYfrnP/+pRx55RJ06dVJoaKimTZumxx9/3F2HAAAA6hiLYRiGu4u4nIqLixUQEKCioiL5+/u7fPyIGWtcPiYAAFeSI88OcfmYzvz+vqJWSwEAAFyK0+EmIiJCTz31lHJycmqjHgAAgN/E6XDz8MMPa+XKlWrdurUGDBig9PR0lZWV1UZtAAAATqtRuNm1a5e2bNmi6667Tg8++KCaN2+uqVOnaseOHbVRIwAAQLXVeM5N165d9fLLL+uHH35QSkqK3nzzTfXo0UOdO3fW4sWLdZXNUwYAAHVEjZeCnzlzRh988IGWLFmi9evXq1evXpo4caK+//57zZw5U5988omWLVvmyloBAAAuyelws2PHDi1ZskTvvfeePDw8FB8fr5deeknt27e397n99tvVo0cPlxYKAABQHU6Hmx49emjAgAF67bXXNGLECHl5eVXq06pVK40dO9YlBQIAADjD6XBz6NAhhYeHX7SPn5+flixZUuOiAAAAasrpCcX5+fn68ssvK7V/+eWX2rZtm0uKAgAAqCmnw80DDzygY8eOVWo/fvw4X2AJAADczulws3//fnXt2rVSe5cuXbR//36XFAUAAFBTTocbq9WqvLy8Su0//vij6tVz65eMAwAAOB9uBg4cqKSkJBUVFdnbCgsLNXPmTA0YMMClxQEAADjL6UstL7zwgvr27avw8HB16dJFkrRr1y4FBwfrnXfecXmBAAAAznA63ISGhmrPnj1KS0vT7t275evrqwkTJmjcuHFVPvMGAADgcqrRJBk/Pz9NmTLF1bUAAAD8ZjWeAbx//37l5OSovLzcof222277zUUBAADUVI2eUHz77bdr7969slgs9m//tlgskqSKigrXVggAAOAEp1dLTZs2Ta1atVJ+fr7q16+vr776Sps3b1b37t21adOmWigRAACg+py+cpOVlaUNGzYoMDBQHh4e8vDwUO/evZWamqqHHnpIO3furI06AQAAqsXpKzcVFRVq2LChJCkwMFA//PCDJCk8PFzffPONa6sDAABwktNXbq6//nrt3r1brVq1UnR0tJ5//nl5e3vr9ddfV+vWrWujRgAAgGpzOtw8+eSTKi0tlSQ99dRTGjp0qPr06aOmTZsqIyPD5QUCAAA4w+lwExcXZ//va6+9Vl9//bVOnjypxo0b21dMAQAAuItTc27OnDmjevXqad++fQ7tTZo0IdgAAIA6walw4+XlpZYtW/IsGwAAUGc5vVrqiSee0MyZM3Xy5MnaqAcAAOA3cXrOzYIFC/Tdd9+pRYsWCg8Pl5+fn8PnO3bscFlxAAAAznI63IwYMaIWygAAAHANp8NNSkpKbdQBAADgEk7PuQEAAKjLnL5y4+HhcdFl36ykAgAA7uR0uPnggw8c3p85c0Y7d+7U22+/rTlz5risMAAAgJpwOtwMHz68UtuoUaP0u9/9ThkZGZo4caJLCgMAAKgJl8256dWrlzIzM101HAAAQI24JNycPn1aL7/8skJDQ10xHAAAQI05fVvq11+QaRiGSkpKVL9+fb377rsuLQ4AAMBZToebl156ySHceHh4KCgoSNHR0WrcuLFLiwMAAHCW0+Fm/PjxtVAGAACAazg952bJkiVavnx5pfbly5fr7bffdklRAAAANeV0uElNTVVgYGCl9mbNmumZZ55xSVEAAAA15XS4ycnJUatWrSq1h4eHKycnxyVFAQAA1JTT4aZZs2bas2dPpfbdu3eradOmLikKAACgppwON+PGjdNDDz2kjRs3qqKiQhUVFdqwYYOmTZumsWPH1kaNAAAA1eb0aqm5c+fqyJEjuuWWW1Sv3i+b22w2xcfHM+cGAAC4ndPhxtvbWxkZGXr66ae1a9cu+fr6qmPHjgoPD6+N+gAAAJzidLg5p02bNmrTpo0rawEAAPjNnJ5zM3LkSD333HOV2p9//nmNHj3aJUUBAADUlNPhZvPmzRo8eHCl9ltvvVWbN292SVEAAAA15XS4OXXqlLy9vSu1e3l5qbi42CVFAQAA1JTT4aZjx47KyMio1J6enq4OHTq4pCgAAICacnpC8axZs/T73/9e2dnZuvnmmyVJmZmZWrZsmVasWOHyAgEAAJzhdLgZNmyYVq1apWeeeUYrVqyQr6+voqKitGHDBjVp0qQ2agQAAKi2Gi0FHzJkiIYMGSJJKi4u1nvvvafp06dr+/btqqiocGmBAAAAznB6zs05mzdvVkJCglq0aKF58+bp5ptv1r///W9X1gYAAOA0p67c5ObmaunSpXrrrbdUXFysO+64Q2VlZVq1ahWTiQEAQJ1Q7Ss3w4YNU7t27bRnzx7Nnz9fP/zwg1555ZXarA0AAMBp1b5y8/HHH+uhhx7Sfffdx9cuAACAOqvaV24+//xzlZSUqFu3boqOjtaCBQtUUFBQm7UBAAA4rdrhplevXnrjjTf0448/6t5771V6erpatGghm82m9evXq6SkpDbrBAAAqBanV0v5+fnpnnvu0eeff669e/fq0Ucf1bPPPqtmzZrptttuq40aAQAAqq3GS8ElqV27dnr++ef1/fff67333nNVTQAAADX2m8LNOZ6enhoxYoRWr15do+0XLlyoiIgI+fj4KDo6Wlu2bKnWdunp6bJYLBoxYkSN9gsAAMzHJeHmt8jIyFBiYqJSUlK0Y8cORUVFKS4uTvn5+Rfd7siRI5o+fbr69OlzmSoFAABXAreHmxdffFGTJ0/WhAkT1KFDBy1atEj169fX4sWLL7hNRUWF7rrrLs2ZM0etW7e+6PhlZWUqLi52eAEAAPNya7gpLy/X9u3bFRsba2/z8PBQbGyssrKyLrjdU089pWbNmmnixImX3EdqaqoCAgLsr7CwMJfUDgAA6ia3hpuCggJVVFQoODjYoT04OFi5ublVbvP555/rrbfe0htvvFGtfSQlJamoqMj+Onbs2G+uGwAA1F01+lZwdykpKdHdd9+tN954Q4GBgdXaxmq1ymq11nJlAACgrnBruAkMDJSnp6fy8vIc2vPy8hQSElKpf3Z2to4cOaJhw4bZ22w2mySpXr16+uabbxQZGVm7RQMAgDrNrbelvL291a1bN2VmZtrbbDabMjMzFRMTU6l/+/bttXfvXu3atcv+uu2229S/f3/t2rWL+TQAAMD9t6USExOVkJCg7t27q2fPnpo/f75KS0s1YcIESVJ8fLxCQ0OVmpoqHx8fXX/99Q7bN2rUSJIqtQMAgKuT28PNmDFjdOLECSUnJys3N1edO3fWunXr7JOMc3Jy5OHh9hXrAADgCmExDMNwdxGXU3FxsQICAlRUVCR/f3+Xjx8xY43LxwQA4Epy5NkhLh/Tmd/fXBIBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmUifCzcKFCxURESEfHx9FR0dry5YtF+z7xhtvqE+fPmrcuLEaN26s2NjYi/YHAABXF7eHm4yMDCUmJiolJUU7duxQVFSU4uLilJ+fX2X/TZs2ady4cdq4caOysrIUFhamgQMH6vjx45e5cgAAUBdZDMMw3FlAdHS0evTooQULFkiSbDabwsLC9OCDD2rGjBmX3L6iokKNGzfWggULFB8fX+nzsrIylZWV2d8XFxcrLCxMRUVF8vf3d92B/H8RM9a4fEwAAK4kR54d4vIxi4uLFRAQUK3f3269clNeXq7t27crNjbW3ubh4aHY2FhlZWVVa4yffvpJZ86cUZMmTar8PDU1VQEBAfZXWFiYS2oHAAB1k1vDTUFBgSoqKhQcHOzQHhwcrNzc3GqN8fjjj6tFixYOAel8SUlJKioqsr+OHTv2m+sGAAB1Vz13F/BbPPvss0pPT9emTZvk4+NTZR+r1Sqr1XqZKwMAAO7i1nATGBgoT09P5eXlObTn5eUpJCTkotu+8MILevbZZ/XJJ5+oU6dOtVkmAAC4grj1tpS3t7e6deumzMxMe5vNZlNmZqZiYmIuuN3zzz+vuXPnat26derevfvlKBUAAFwh3H5bKjExUQkJCerevbt69uyp+fPnq7S0VBMmTJAkxcfHKzQ0VKmpqZKk5557TsnJyVq2bJkiIiLsc3MaNGigBg0auO04AABA3eD2cDNmzBidOHFCycnJys3NVefOnbVu3Tr7JOOcnBx5ePzvAtNrr72m8vJyjRo1ymGclJQUzZ49+3KWDgAA6iC3P+fmcnNmnXxN8JwbAMDV7qp+zg0AAICrEW4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICp1Ilws3DhQkVERMjHx0fR0dHasmXLRfsvX75c7du3l4+Pjzp27Ki1a9depkoBAEBd5/Zwk5GRocTERKWkpGjHjh2KiopSXFyc8vPzq+z/xRdfaNy4cZo4caJ27typESNGaMSIEdq3b99lrhwAANRFFsMwDHcWEB0drR49emjBggWSJJvNprCwMD344IOaMWNGpf5jxoxRaWmpPvroI3tbr1691LlzZy1atOiS+ysuLlZAQICKiork7+/vugP5/yJmrHH5mAAAXEmOPDvE5WM68/u7nsv37oTy8nJt375dSUlJ9jYPDw/FxsYqKyurym2ysrKUmJjo0BYXF6dVq1ZV2b+srExlZWX290VFRZJ+OUm1wVb2U62MCwDAlaI2fseeG7M612TcGm4KCgpUUVGh4OBgh/bg4GB9/fXXVW6Tm5tbZf/c3Nwq+6empmrOnDmV2sPCwmpYNQAAuJiA+bU3dklJiQICAi7ax63h5nJISkpyuNJjs9l08uRJNW3aVBaLxY2VAXC14uJihYWF6dixY7Vy2xmA+xiGoZKSErVo0eKSfd0abgIDA+Xp6am8vDyH9ry8PIWEhFS5TUhIiFP9rVarrFarQ1ujRo1qXjSAOs/f359wA5jQpa7YnOPW1VLe3t7q1q2bMjMz7W02m02ZmZmKiYmpcpuYmBiH/pK0fv36C/YHAABXF7fflkpMTFRCQoK6d++unj17av78+SotLdWECRMkSfHx8QoNDVVqaqokadq0aerXr5/mzZunIUOGKD09Xdu2bdPrr7/uzsMAAAB1hNvDzZgxY3TixAklJycrNzdXnTt31rp16+yThnNycuTh8b8LTDfccIOWLVumJ598UjNnzlSbNm20atUqXX/99e46BAB1hNVqVUpKSqVb0QCuLm5/zg0AAIAruf0JxQAAAK5EuAEAAKZCuAEAAKZCuAEAAKZCuAFQJ0RERGj+/Pku7wvg6kO4AXBB48ePl8VikcVikZeXl4KDgzVgwAAtXrxYNpvNpfvaunWrpkyZ4vK+NXH+cVf1ioiIqLV9A/jtWAoO4ILGjx+vvLw8LVmyRBUVFcrLy9O6deuUmpqqPn36aPXq1apXz+2Py3K5oqIinT592v6+efPmWrJkiQYNGiRJ8vT0VFBQkP3z8vJyeXt7X/Y6AVSNKzcALspqtSokJEShoaHq2rWrZs6cqQ8//FAff/yxli5dau9XWFioSZMmKSgoSP7+/rr55pu1e/duh7H+8Y9/qEePHvLx8VFgYKBuv/12+2fn32oyDEOzZ89Wy5YtZbVa1aJFCz300ENV9pV+edjn8OHD1aBBA/n7++uOO+5w+A662bNnq3PnznrnnXcUERGhgIAAjR07ViUlJVUec0BAgEJCQuwv6ZfvpDv3vkePHpo7d67i4+Pl7+9vv4r0+eefq0+fPvL19VVYWJgeeughlZaW2sctKyvT9OnTFRoaKj8/P0VHR2vTpk1O/TwAXBrhBoDTbr75ZkVFRWnlypX2ttGjRys/P18ff/yxtm/frq5du+qWW27RyZMnJUlr1qzR7bffrsGDB2vnzp3KzMxUz549qxz//fff10svvaS//vWvOnjwoFatWqWOHTtW2ddms2n48OE6efKkPv30U61fv16HDh3SmDFjHPplZ2dr1apV+uijj/TRRx/p008/1bPPPlvjc/DCCy8oKipKO3fu1KxZs5Sdna1BgwZp5MiR2rNnjzIyMvT5559r6tSp9m2mTp2qrKwspaena8+ePRo9erQGDRqkgwcP1rgOAFUwAOACEhISjOHDh1f52ZgxY4zrrrvOMAzD+Oyzzwx/f3/j559/dugTGRlp/PWvfzUMwzBiYmKMu+6664L7Cg8PN1566SXDMAxj3rx5Rtu2bY3y8vJL9v3Xv/5leHp6Gjk5OfbPv/rqK0OSsWXLFsMwDCMlJcWoX7++UVxcbO/z2GOPGdHR0Rc++PNIMj744AOH/Y8YMcKhz8SJE40pU6Y4tH322WeGh4eHcfr0aePo0aOGp6encfz4cYc+t9xyi5GUlFStOgBUj/lulgO4LAzDkMVikSTt3r1bp06dUtOmTR36nD59WtnZ2ZKkXbt2afLkydUae/To0Zo/f75at26tQYMGafDgwRo2bFiV83sOHDigsLAwhYWF2ds6dOigRo0a6cCBA+rRo4ekX25lNWzY0N6nefPmys/Pd+6gz9O9e3eH97t379aePXuUlpZmbzMMQzabTYcPH9ahQ4dUUVGhtm3bOmxXVlZW6bwB+G0INwBq5MCBA2rVqpUk6dSpU2revHmV80caNWokSfL19a322GFhYfrmm2/0ySefaP369br//vv15z//WZ9++qm8vLxqVO+vt7NYLL9pxZefn5/D+1OnTunee+91mBt0TsuWLbVnzx55enpq+/bt8vT0dPi8QYMGNa4DQGWEGwBO27Bhg/bu3atHHnlEktS1a1fl5uaqXr16F1wm3alTJ2VmZmrChAnV2oevr6+GDRumYcOG6YEHHlD79u21d+9ede3a1aHfddddp2PHjunYsWP2qzf79+9XYWGhOnToUPODdFLXrl21f/9+XXvttVV+3qVLF1VUVCg/P199+vS5bHUBVyPCDYCLKisrU25ubqWl4EOHDlV8fLwkKTY2VjExMRoxYoSef/55tW3bVj/88IN9EnH37t2VkpKiW265RZGRkRo7dqzOnj2rtWvX6vHHH6+0z6VLl6qiokLR0dGqX7++3n33Xfn6+io8PLxS39jYWHXs2FF33XWX5s+fr7Nnz+r+++9Xv379Kt06qk2PP/64evXqpalTp2rSpEny8/PT/v37tX79ei1YsEBt27bVXXfdpfj4eM2bN09dunTRiRMnlJmZqU6dOmnIkCGXrVbA7FgtBeCi1q1bp+bNmysiIkKDBg3Sxo0b9fLLL+vDDz+0316xWCxau3at+vbtqwkTJqht27YaO3asjh49quDgYEnSTTfdpOXLl2v16tXq3Lmzbr75Zm3ZsqXKfTZq1EhvvPGGbrzxRnXq1EmffPKJ/vGPf1Q5N8VisejDDz9U48aN1bdvX8XGxqp169bKyMiovZNShU6dOunTTz/Vt99+qz59+qhLly5KTk5WixYt7H2WLFmi+Ph4Pfroo2rXrp1GjBihrVu3qmXLlpe1VsDseIgfAAAwFa7cAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAU/l/C0OlVGBpEVQAAAAASUVORK5CYII=\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from sklearn.metrics import classification_report\n",
-        "\n",
-        "report = classification_report(y_test, y_pred, output_dict=True)\n",
-        "\n",
-        "metrics = ['precision', 'recall', 'f1-score']\n",
-        "class_0 = [report['0'][m] for m in metrics]\n",
-        "class_1 = [report['1'][m] for m in metrics]\n",
-        "\n",
-        "x = np.arange(len(metrics))\n",
-        "width = 0.35\n",
-        "\n",
-        "plt.figure()\n",
-        "plt.bar(x - width/2, class_0, width, label='Kelas 0')\n",
-        "plt.bar(x + width/2, class_1, width, label='Kelas 1')\n",
-        "\n",
-        "plt.xticks(x, metrics)\n",
-        "plt.ylabel('Score')\n",
-        "plt.title('Precision, Recall, dan F1-Score per Kelas')\n",
-        "plt.legend()\n",
-        "plt.ylim(0, 1)\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 452
-        },
-        "id": "ZU8PYqK2mKs7",
-        "outputId": "545583af-bd93-4c8c-950f-d827c6198945"
-      },
-      "execution_count": 267,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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ztWnTJs8vsDp16kiSDh48WOAvWBcXF3Xr1k3dunVTVlaWhg4dqnfeeUdRUVE5LuvGH5PExETrYeobEhMTb9sfm7zcCBkdO3bUnDlzNH78eNWuXVvSH6cebnWEoU6dOjlesXKzVatWqXbt2vr4449tjqLExMQUfQPyEBAQoIMHD8owDJv1JiYm2vQ7cOCAfvrpJy1dulTh4eHW9oKccrSH8PBwtW3b1vo8P3+oW7Rooe3bt+vUqVMKCAjI1/sXEBCg7777TllZWTb/ibhx08Fb7bcF2Z/ycvjw4WxtP/30k/XKsuJaz61UqVJF//nPf9SmTRs9/vjj2rJli0JCQiT98XlISEhQp06d8jytnZNVq1apY8eOWrhwoU37+fPn5e3tbdNWvnx59e7dW71791Z6eroef/xxvfLKK5owYUKpvP2EGTHmBnbx5JNPKjMzU1OnTs02LSMjQ+fPn5ckde7cWZ6enoqNjdX169dt+hmGkevyf/31V5vnjo6OuvfeeyUp10s+W7RoIR8fH82fP9+mz6effqpDhw6pa9eu+dq24tahQwe1bNlSs2bN0vXr1+Xj46MOHTronXfe0alTp7L1v/k+KT179lRCQoLWrFmTrd+N1+/GkY6bX8+vvvpK8fHxxb0pNrp06aL//ve/WrVqlbXt6tWr2e79klN9hmHYXNZfGtWuXVuhoaHWx43TEikpKdbLr2+Wnp6urVu3ytHR0Rq+8/P+denSRSkpKVq+fLl1WkZGht566y15eHioffv2edZZkP0pL2vXrrUZM7Nnzx599dVXeuSRR4p1PflRvXp1bdmyReXLl1fXrl114MABSX9875w8eVILFizINs+1a9fyvCLLyckp23fOypUrs40T+vN3j4uLixo2bCjDMGxO+eL24sgN7KJ9+/Z67rnnFBsbq/3796tz585ydnbW4cOHtXLlSs2ePVu9evWSl5eX3njjDQ0cOFD33XefnnrqKVWqVEkJCQm6evVqrqeYBg4cqHPnzunBBx9UjRo1dOzYMb311ltq2rSpdSzCnzk7O+u1117TgAED1L59e/Xp08d6KXhgYKBGjRpVqG3t37+/li5dar3xW2GMGTNGTzzxhJYsWaIhQ4Zo7ty5atu2rRo3bqxBgwapdu3aSk1NVXx8vE6cOGG998aYMWO0atUqPfHEE3rmmWcUHBysc+fOad26dZo/f76aNGmiRx99VB9//LEee+wxde3aVUlJSZo/f74aNmxoM2Ykv25cTr948eI8f5tn0KBBmjNnjsLDw7V3715VrVpVH3zwQbZLo4OCglSnTh2NHj1aJ0+elJeXl1avXl3ksU1F8cEHH+jYsWO6evWqJOmLL76w3k/l6aefzvNoyYkTJ9SyZUs9+OCD6tSpk/z8/HT69Gn961//UkJCgkaOHGk9EpCf92/w4MF655131L9/f+3du1eBgYFatWqVdu3apVmzZsnT0/OW25Pf/SkvdevWVdu2bfX8888rLS1Ns2bNUpUqVWwGYxfHevKrXr162rx5szp06KCwsDDt3LlTTz/9tFasWKEhQ4Zo27ZtatOmjTIzM/Xjjz9qxYoV2rx5c66n2R999FFNmTJFAwYMUOvWrXXgwAH985//tB6RuqFz587y8/NTmzZt5Ovrq0OHDmnOnDnq2rVrvt4LFBO7XKOFMu/GpZpff/11nv369etnlC9fPtfp7777rhEcHGy4ubkZnp6eRuPGjY2xY8ca//3vf236rVu3zmjdurXh5uZmeHl5GS1btjT+9a9/2azn5kvBV61aZXTu3Nnw8fExXFxcjJo1axrPPfeccerUKWufP18KfsPy5cuNZs2aGRaLxahcubLRt29fm0tc89qumJgY488fq549expubm7Gb7/9luvrYBh5v6aZmZlGnTp1jDp16lgvjT169KgRHh5u+Pn5Gc7Ozkb16tWNRx991Fi1apXNvL/++qsRERFhVK9e3XBxcTFq1Khh9OvXzzh79qxhGH9cijtt2jQjICDAsFgsRrNmzYz169dne00NI3+Xgr/11luGJGPTpk15bq9hGMaxY8eM7t27G+7u7oa3t7cxYsQI6+0Abn5ffvjhByM0NNTw8PAwvL29jUGDBhkJCQmGJGPx4sXWfgV5X3LSvn37fF3Gm9sl4zntT3928eJFY/bs2UZYWJhRo0YNw9nZ2fD09DRCQkKMBQsW2NziwDBu/f4ZhmGkpqYaAwYMMLy9vQ0XFxejcePGNq+LYfzvcunXX389x7ryuz/92c3LnTFjhuHv729YLBajXbt2RkJCQqHWk9/vl/xs244dOww3NzejVq1axsmTJ4309HTjtddeMxo1amRYLBajUqVKRnBwsDF58mTjwoUL1vlyuhT8xRdfNKpWrWq4ubkZbdq0MeLj44327dsb7du3t/Z75513jAceeMCoUqWKYbFYjDp16hhjxoyxWTZuPwfDyOPYPoAi8/X1VXh4uF5//XV7l1IinnzySf3yyy/as2ePvUtBCfjll19Uq1Ytvf766xo9erS9ywEkcVoKuK2+//57Xbt2TePGjbN3KSXCMAzFxcXpww8/tHcpAO5ghBvgNmrUqJEuXrxo7zJKjIODQ573CgGAksDVUgAAwFTsGm6++OILdevWTdWqVZODg0OeP1p2Q1xcnJo3by6LxaK6detqyZIlt71OAEDOAgMDZRgG421Qqtg13Fy5ckVNmjTR3Llz89U/KSlJXbt2VceOHbV//36NHDlSAwcO1ObNm29zpQAAoKwoNVdLOTg4aM2aNerRo0eufcaNG6cNGzbY3LHzr3/9q86fP69NmzaVQJUAAKC0K1MDiuPj47PdtjssLEwjR47MdZ60tDSbu83e+NXjKlWqFPj22wAAwD4Mw9ClS5dUrVq1bL9X+GdlKtykpKTI19fXps3X11cXL17UtWvXcvztltjYWH6sDAAAkzh+/Lhq1KiRZ58yFW4KY8KECYqMjLQ+v3DhgmrWrKnjx4/Ly8vLjpWVcbF571imMuGEvSsoFvfE3Blj0w5OzvkXpwGUbRcvXpS/v3++fsaiTIUbPz8/paam2rSlpqbKy8sr11/ctVgsslgs2dq9vLwIN0VhuYNO6ZlkP3G0uN+6kwnwuS57AsdvsHcJJeKXV+3z47tmk58hJWXqPjchISHaunWrTdvNP2cPAABg1yM3ly9f1pEjR6zPk5KStH//flWuXFk1a9bUhAkTdPLkSb3//vuSpCFDhmjOnDkaO3asnnnmGX3++edasWKFNmwoPan/jvkfiKu9KwAAIGd2PXLzzTffqFmzZmrWrJkkKTIyUs2aNVN0dLQk6dSpU0pOTrb2r1WrljZs2KAtW7aoSZMmmjFjht577z2FhXGOHQAA/MGuR246dOigvG6zk9Pdhzt06KB9+/bdxqqAO5eDJE+Lo8o7O8ixjA6run79ur1LKBInJyeVK1eOW1UARVCmBhQDuH0quzqqT2NPNfZ1ldMt7iFRmiUlJdm7hCJzd3dX1apV5eLiYu9SgDKJcANA5Ryk8W0ry7+Kh9y8KsnBqex+NdTyK7tXSxmGofT0dJ05c0ZJSUmqV6/eLW9WBiC7svsNBqDY3FXeSZXcy8m9orccnLPfOqEscXUt26Pd3dzc5OzsrGPHjik9Pb3Mbw9gD/yXAMD/j69xkBjnUSpwtAYoGj5BAADAVAg3AADAVBhzAyBP3efsKtH1rYtoU6Lrk/64nfuaNWvUo0ePEl83gOLHkRsAZVrUqKEa+Wxfm7ZVq1bJ1dVVM2bMsFNVtq5fv65hw4apSpUq8vDwUM+ePbP9Th6A4kO4AWAq7733nvr27at58+bpxRdftHc5kqRRo0bp3//+t1auXKnt27frv//9rx5//HF7lwWYFuEGgGksnjdbL7zwgpYtW6YBAwZY2z/55BM1b95crq6uql27tiZPnqyMjIxclzNu3DjVr19f7u7uql27tqKiovT7779bpyckJKhjx47y9PSUl5eXgoOD9c033+S4rAsXLmjhwoWaOXOmHnzwQQUHB2vx4sXavXu3vvzyy+LbeABWjLkBYApvTIvRivcXaf369erUqZO1fceOHQoPD9ebb76pdu3a6ejRoxo8eLAkKSYmJsdleXp6asmSJapWrZoOHDigQYMGydPTU2PHjpUk9e3bV82aNdO8efPk5OSk/fv3y9nZOcdl7d27V7///rtCQ0OtbUFBQapZs6bi4+N1//33F9dLAOD/EW4AlHk74z7Ttv9s1LvLPrEJNpI0efJkjR8/Xv369ZMk1a5dW1OnTtXYsWNzDTcvvfSS9d+BgYEaPXq0li1bZg03ycnJGjNmjIKCgiRJ9erVy7W2lJQUubi4qGLFijbtvr6+SklJKfC2Arg1wg2AMq9+UCP99tuvmjcjVn26PigPDw/rtISEBO3atUuvvPKKtS0zM1PXr1/X1atX5e7unm15y5cv15tvvqmjR4/q8uXLysjIkJfX/37WITIyUgMHDtQHH3yg0NBQPfHEE6pTp87t3UgA+caYGwBlno9fVS1c8W+dTj2lhx9+WJcuXbJOu3z5siZPnqz9+/dbHwcOHNDhw4dz/GmD+Ph49e3bV126dNH69eu1b98+TZw4Uenp6dY+kyZN0vfff6+uXbvq888/V8OGDbVmzZoca/Pz81N6errOnz9v056amio/P7/ieQEA2CDcADCFajVqatHK9UpJSbEJOM2bN1diYqLq1q2b7ZHTzxzs3r1bAQEBmjhxolq0aKF69erp2LFj2frVr19fo0aN0n/+8x89/vjjWrx4cY51BQcHy9nZWVu3brW2JSYmKjk5WSEhIcW09QBuxmkpAKbhV62G4uLi1LFjR4WFhWnTpk2Kjo7Wo48+qpo1a6pXr15ydHRUQkKCDh48qJdffjnbMurVq6fk5GQtW7ZM9913nzZs2GBzVObatWsaM2aMevXqpVq1aunEiRP6+uuv1bNnzxxrqlChgp599llFRkaqcuXK8vLy0gsvvKCQkBAGEwO3CeEGQJ7sccfgoqhRwzbgbN68WevXr9eUKVP02muvydnZWUFBQRo4cGCO83fv3l2jRo1SRESE0tLS1LVrV0VFRWnSpEmSJCcnJ/36668KDw9XamqqvL299fjjj2vy5Mm51vTGG2/I0dFRPXv2VFpamsLCwvT222/fjs0HIMnBMAzD3kWUpIsXL6pChQq6cOGCzQDB4hI4fkOxL7M0+sX1KXuXUHImXbB3BcUir32zuqeTJnX0kU+1GnIo51KCVRW/e2tUtHcJRXb9+nUlJSWpVq1aOY4LMps75nvz1a72LqFMK8jfb8bcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAU+EOxQAAlIRJFexdQcmx881PCTcA8nTvewElur7vBmb/kcrbzcHBQWvWrFGPHj1KfN0Aih+npQCUaVGjhmrks31t2latWiVXV1fNmDHDTlXZevfdd9WhQwd5eXnJwcFB58+ft3dJgKkRbgCYynvvvae+fftq3rx5evHFF+1djiTp6tWrevjhh/X3v//d3qUAdwTCDQDTWDxvtl544QUtW7ZMAwYMsLZ/8sknat68uVxdXVW7dm1NnjxZGRkZuS5n3Lhxql+/vtzd3VW7dm1FRUXp999/t05PSEhQx44d5enpKS8vLwUHB+ubb77JdXkjR47U+PHjdf/99xfPhgLIE2NuAJjCG9NitOL9RVq/fr06depkbd+xY4fCw8P15ptvql27djp69KgGDx4sSYqJiclxWZ6enlqyZImqVaumAwcOaNCgQfL09NTYsWMlSX379lWzZs00b948OTk5af/+/XJ2dr79GwkgXwg3AMq8nXGfadt/NurdZZ/YBBtJmjx5ssaPH69+/fpJkmrXrq2pU6dq7NixuYabl156yfrvwMBAjR49WsuWLbOGm+TkZI0ZM0ZBQUGSpHr16t2OzQJQSIQbAGVe/aBG+u23XzVvRqz6dH1QHh4e1mkJCQnatWuXXnnlFWtbZmamrl+/rqtXr8rd3T3b8pYvX64333xTR48e1eXLl5WRkSEvLy/r9MjISA0cOFAffPCBQkND9cQTT6hOnTq3dyMB5BtjbgCUeT5+VbVwxb91OvWUHn74YV26dMk67fLly5o8ebL2799vfRw4cECHDx+Wq6trtmXFx8erb9++6tKli9avX699+/Zp4sSJSk9Pt/aZNGmSvv/+e3Xt2lWff/65GjZsqDVr1pTItgK4NY7cADCFajVqatHK9Rr6VA89/PDD2rRpkzw9PdW8eXMlJiaqbt26+VrO7t27FRAQoIkTJ1rbjh3Lfu+d+vXrq379+ho1apT69OmjxYsX67HHHiu27QFQeIQbAKbhV62G4uLi1LFjR4WFhWnTpk2Kjo7Wo48+qpo1a6pXr15ydHRUQkKCDh48qJdffjnbMurVq6fk5GQtW7ZM9913nzZs2GBzVObatWsaM2aMevXqpVq1aunEiRP6+uuv1bNnz1zrSklJUUpKio4cOSJJOnDggDw9PVWzZk1Vrly5+F8I4A5HuAGQJ3vcMbgoatSwDTibN2/W+vXrNWXKFL322mtydnZWUFCQBg4cmOP83bt316hRoxQREaG0tDR17dpVUVFRmjRpkiTJyclJv/76q8LDw5Wamipvb289/vjjmjx5cq41zZ8/32b6Aw88IElavHix+vfvX2zbDuAPDoZhGPYuoiRdvHhRFSpU0IULF2wGCBaXwPEbin2ZpdEvrk/Zu4SSY+ffSCkuee2b1T2dNKmjj3yq1ZBDOZcSrKr43Vujor1LKLLr168rKSlJtWrVynFckNnwvWlCt+F7syB/vxlQDAAATIVwAwAATIVwAwAATIVwAwAATIVwA0BZhiQZ0p11fUGpdYdd5wEUO8INAJ2/nqXfMw0ZGem37ozb7urVq5LEj3EChcR9bgDoWoahrT9f1qMuTqpUWX9cDu7gYO+yCuX69ev2LqHQDMPQ1atXdfr0aVWsWFFOTk72Lgkokwg3ACRJHx+6IknqVDtTzk4OkspmuHG55mbvEoqsYsWK8vPzs3cZQJlFuAEgSTIkrT50RRsOX1UlV0c5ls1so60vdrB3CUXi7OzMERugiAg3AGxczzB06nKmvcsotDvhjr4A8saAYgAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCpcLQXAXCZVsHcFJWfSBXtXAJRKHLkBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmYvdwM3fuXAUGBsrV1VWtWrXSnj178uw/a9Ys3X333XJzc5O/v79GjRql69evl1C1AACgtLNruFm+fLkiIyMVExOjb7/9Vk2aNFFYWJhOnz6dY/+PPvpI48ePV0xMjA4dOqSFCxdq+fLl+vvf/17ClQMAgNLKruFm5syZGjRokAYMGKCGDRtq/vz5cnd316JFi3Lsv3v3brVp00ZPPfWUAgMD1blzZ/Xp0+eWR3sAAMCdw27hJj09XXv37lVoaOj/inF0VGhoqOLj43Ocp3Xr1tq7d681zPz888/auHGjunTpkut60tLSdPHiRZsHAAAwL7v9cObZs2eVmZkpX19fm3ZfX1/9+OOPOc7z1FNP6ezZs2rbtq0Mw1BGRoaGDBmS52mp2NhYTZ48uVhrBwAApZfdBxQXRFxcnKZNm6a3335b3377rT7++GNt2LBBU6dOzXWeCRMm6MKFC9bH8ePHS7BiAABQ0ux25Mbb21tOTk5KTU21aU9NTZWfn1+O80RFRenpp5/WwIEDJUmNGzfWlStXNHjwYE2cOFGOjtmzmsVikcViKf4NAAAApZLdjty4uLgoODhYW7dutbZlZWVp69atCgkJyXGeq1evZgswTk5OkiTDMG5fsQAAoMyw25EbSYqMjFS/fv3UokULtWzZUrNmzdKVK1c0YMAASVJ4eLiqV6+u2NhYSVK3bt00c+ZMNWvWTK1atdKRI0cUFRWlbt26WUMOAAC4s9k13PTu3VtnzpxRdHS0UlJS1LRpU23atMk6yDg5OdnmSM1LL70kBwcHvfTSSzp58qTuuusudevWTa+88oq9NgEAAJQydg03khQREaGIiIgcp8XFxdk8L1eunGJiYhQTE1MClQEAgLKoTF0tBQAAcCuEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCp2Dzdz585VYGCgXF1d1apVK+3ZsyfP/ufPn9ewYcNUtWpVWSwW1a9fXxs3biyhagEAQGlXzp4rX758uSIjIzV//ny1atVKs2bNUlhYmBITE+Xj45Otf3p6uh566CH5+Pho1apVql69uo4dO6aKFSuWfPEAAKBUsmu4mTlzpgYNGqQBAwZIkubPn68NGzZo0aJFGj9+fLb+ixYt0rlz57R79245OztLkgIDA0uyZAAAUMrZ7bRUenq69u7dq9DQ0P8V4+io0NBQxcfH5zjPunXrFBISomHDhsnX11f33HOPpk2bpszMzFzXk5aWposXL9o8AACAedkt3Jw9e1aZmZny9fW1aff19VVKSkqO8/z8889atWqVMjMztXHjRkVFRWnGjBl6+eWXc11PbGysKlSoYH34+/sX63YAAIDSxe4DigsiKytLPj4+evfddxUcHKzevXtr4sSJmj9/fq7zTJgwQRcuXLA+jh8/XoIVAwCAkma3MTfe3t5ycnJSamqqTXtqaqr8/PxynKdq1apydnaWk5OTta1BgwZKSUlRenq6XFxcss1jsVhksViKt3gAAFBq2e3IjYuLi4KDg7V161ZrW1ZWlrZu3aqQkJAc52nTpo2OHDmirKwsa9tPP/2kqlWr5hhsAADAnceup6UiIyO1YMECLV26VIcOHdLzzz+vK1euWK+eCg8P14QJE6z9n3/+eZ07d04jRozQTz/9pA0bNmjatGkaNmyYvTYBAACUMna9FLx37946c+aMoqOjlZKSoqZNm2rTpk3WQcbJyclydPxf/vL399fmzZs1atQo3XvvvapevbpGjBihcePG2WsTAABAKWPXcCNJERERioiIyHFaXFxctraQkBB9+eWXt7kqAABQVpWpq6UAAABuhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMpUjhJj09XYmJicrIyCiuegAAAIqkUOHm6tWrevbZZ+Xu7q5GjRopOTlZkvTCCy/o1VdfLdYCAQAACqJQ4WbChAlKSEhQXFycXF1dre2hoaFavnx5sRUHAABQUOUKM9PatWu1fPly3X///XJwcLC2N2rUSEePHi224gAAAAqqUEduzpw5Ix8fn2ztV65csQk7AAAAJa1Q4aZFixbasGGD9fmNQPPee+8pJCSkeCoDAAAohEKdlpo2bZoeeeQR/fDDD8rIyNDs2bP1ww8/aPfu3dq+fXtx1wgAAJBvhTpy07ZtWyUkJCgjI0ONGzfWf/7zH/n4+Cg+Pl7BwcHFXSMAAEC+FfjIze+//67nnntOUVFRWrBgwe2oCQAAoNAKfOTG2dlZq1evvh21AAAAFFmhTkv16NFDa9euLeZSAAAAiq5QA4rr1aunKVOmaNeuXQoODlb58uVtpg8fPrxYigMAACioQoWbhQsXqmLFitq7d6/27t1rM83BwYFwAwAA7KZQ4SYpKam46wAAACgWRfpVcEkyDEOGYRRHLQAAAEVW6HDz/vvvq3HjxnJzc5Obm5vuvfdeffDBB8VZGwAAQIEV6rTUzJkzFRUVpYiICLVp00aStHPnTg0ZMkRnz57VqFGjirVIAACA/CpUuHnrrbc0b948hYeHW9u6d++uRo0aadKkSYQbAABgN4U6LXXq1Cm1bt06W3vr1q116tSpIhcFAABQWIUKN3Xr1tWKFSuytS9fvlz16tUrclEAAACFVajTUpMnT1bv3r31xRdfWMfc7Nq1S1u3bs0x9AAAAJSUQh256dmzp7766it5e3tr7dq1Wrt2rby9vbVnzx499thjxV0jAABAvhXqyI0kBQcH68MPPyzOWgAAAIqsUEduNm7cqM2bN2dr37x5sz799NMiFwUAAFBYhQo348ePV2ZmZrZ2wzA0fvz4IhcFAABQWIUKN4cPH1bDhg2ztQcFBenIkSNFLgoAAKCwChVuKlSooJ9//jlb+5EjR1S+fPkiFwUAAFBYhQo3f/nLXzRy5EgdPXrU2nbkyBG9+OKL6t69e7EVBwAAUFCFCjf/+Mc/VL58eQUFBalWrVqqVauWgoKCVKVKFU2fPr24awQAAMi3Ql0KXqFCBe3evVtbtmxRQkKC3Nzc1KRJE7Vr16646wMAACiQAh25iY+P1/r16yVJDg4O6ty5s3x8fDR9+nT17NlTgwcPVlpa2m0pFAAAID8KFG6mTJmi77//3vr8wIEDGjRokB566CGNHz9e//73vxUbG1vsRQIAAORXgcLN/v371alTJ+vzZcuWqWXLllqwYIEiIyP15ptv8ttSAADArgoUbn777Tf5+vpan2/fvl2PPPKI9fl9992n48ePF191AAAABVSgcOPr66ukpCRJUnp6ur799lvdf//91umXLl2Ss7Nz8VYIAABQAAUKN126dNH48eO1Y8cOTZgwQe7u7jZXSH333XeqU6dOsRcJAACQXwW6FHzq1Kl6/PHH1b59e3l4eGjp0qVycXGxTl+0aJE6d+5c7EUCAADkV4HCjbe3t7744gtduHBBHh4ecnJyspm+cuVKeXh4FGuBAAAABVHom/jlpHLlykUqBgAAoKgK9fMLAAAApRXhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmEqpCDdz585VYGCgXF1d1apVK+3Zsydf8y1btkwODg7q0aPH7S0QAACUGXYPN8uXL1dkZKRiYmL07bffqkmTJgoLC9Pp06fznO+XX37R6NGj1a5duxKqFAAAlAV2DzczZ87UoEGDNGDAADVs2FDz58+Xu7u7Fi1alOs8mZmZ6tu3ryZPnqzatWvnufy0tDRdvHjR5gEAAMzLruEmPT1de/fuVWhoqLXN0dFRoaGhio+Pz3W+KVOmyMfHR88+++wt1xEbG6sKFSpYH/7+/sVSOwAAKJ3sGm7Onj2rzMxM+fr62rT7+voqJSUlx3l27typhQsXasGCBflax4QJE3ThwgXr4/jx40WuGwAAlF7l7F1AQVy6dElPP/20FixYIG9v73zNY7FYZLFYbnNlAACgtLBruPH29paTk5NSU1Nt2lNTU+Xn55et/9GjR/XLL7+oW7du1rasrCxJUrly5ZSYmKg6derc3qIBAECpZtfTUi4uLgoODtbWrVutbVlZWdq6datCQkKy9Q8KCtKBAwe0f/9+66N79+7q2LGj9u/fz3gaAABg/9NSkZGR6tevn1q0aKGWLVtq1qxZunLligYMGCBJCg8PV/Xq1RUbGytXV1fdc889NvNXrFhRkrK1AwCAO5Pdw03v3r115swZRUdHKyUlRU2bNtWmTZusg4yTk5Pl6Gj3K9YBAEAZYfdwI0kRERGKiIjIcVpcXFye8y5ZsqT4CwIAAGUWh0QAAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICplIpwM3fuXAUGBsrV1VWtWrXSnj17cu27YMECtWvXTpUqVVKlSpUUGhqaZ38AAHBnsXu4Wb58uSIjIxUTE6Nvv/1WTZo0UVhYmE6fPp1j/7i4OPXp00fbtm1TfHy8/P391blzZ508ebKEKwcAAKWR3cPNzJkzNWjQIA0YMEANGzbU/Pnz5e7urkWLFuXY/5///KeGDh2qpk2bKigoSO+9956ysrK0devWHPunpaXp4sWLNg8AAGBedg036enp2rt3r0JDQ61tjo6OCg0NVXx8fL6WcfXqVf3++++qXLlyjtNjY2NVoUIF68Pf379YagcAAKWTXcPN2bNnlZmZKV9fX5t2X19fpaSk5GsZ48aNU7Vq1WwC0s0mTJigCxcuWB/Hjx8vct0AAKD0KmfvAori1Vdf1bJlyxQXFydXV9cc+1gsFlkslhKuDAAA2Itdw423t7ecnJyUmppq056amio/P788550+fbpeffVVffbZZ7r33ntvZ5kAAKAMsetpKRcXFwUHB9sMBr4xODgkJCTX+f7xj39o6tSp2rRpk1q0aFESpQIAgDLC7qelIiMj1a9fP7Vo0UItW7bUrFmzdOXKFQ0YMECSFB4erurVqys2NlaS9Nprryk6OlofffSRAgMDrWNzPDw85OHhYbftAAAApYPdw03v3r115swZRUdHKyUlRU2bNtWmTZusg4yTk5Pl6Pi/A0zz5s1Tenq6evXqZbOcmJgYTZo0qSRLBwAApZDdw40kRUREKCIiIsdpcXFxNs9/+eWX218QAAAos+x+Ez8AAIDiRLgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmUirCzdy5cxUYGChXV1e1atVKe/bsybP/ypUrFRQUJFdXVzVu3FgbN24soUoBAEBpZ/dws3z5ckVGRiomJkbffvutmjRporCwMJ0+fTrH/rt371afPn307LPPat++ferRo4d69OihgwcPlnDlAACgNLJ7uJk5c6YGDRqkAQMGqGHDhpo/f77c3d21aNGiHPvPnj1bDz/8sMaMGaMGDRpo6tSpat68uebMmVPClQMAgNKonD1Xnp6err1792rChAnWNkdHR4WGhio+Pj7HeeLj4xUZGWnTFhYWprVr1+bYPy0tTWlpadbnFy5ckCRdvHixiNXnLCvt6m1Zbmlz0cGwdwkl5zbtKyWNfdOE2DfLFPbNoi7yj2Uaxq1fR7uGm7NnzyozM1O+vr427b6+vvrxxx9znCclJSXH/ikpKTn2j42N1eTJk7O1+/v7F7JqSFIFexdQkl69o7a2zLuj3i32zTLljnq3buO+eenSJVWokPfy7RpuSsKECRNsjvRkZWXp3LlzqlKlihwcHOxYWdl18eJF+fv76/jx4/Ly8rJ3OYAV+yZKK/bNojMMQ5cuXVK1atVu2deu4cbb21tOTk5KTU21aU9NTZWfn1+O8/j5+RWov8VikcVisWmrWLFi4YuGlZeXFx9SlErsmyit2DeL5lZHbG6w64BiFxcXBQcHa+vWrda2rKwsbd26VSEhITnOExISYtNfkrZs2ZJrfwAAcGex+2mpyMhI9evXTy1atFDLli01a9YsXblyRQMGDJAkhYeHq3r16oqNjZUkjRgxQu3bt9eMGTPUtWtXLVu2TN98843effdde24GAAAoJewebnr37q0zZ84oOjpaKSkpatq0qTZt2mQdNJycnCxHx/8dYGrdurU++ugjvfTSS/r73/+uevXqae3atbrnnnvstQl3HIvFopiYmGyn+wB7Y99EacW+WbIcjPxcUwUAAFBG2P0mfgAAAMWJcAMAAEyFcAMAAEyFcAMAAEyFcIMCi4uLk4ODg86fP1+sfQF7mDRpkpo2bWp93r9/f/Xo0cNu9aDkGYahwYMHq3LlynJwcND+/fvtXRKKiHCDAmvdurVOnTqVrztFFqQvANjDpk2btGTJEq1fv16nTp3SxYsX1a1bN1WrVk0ODg65/jAzSi/CzR0mPT29yMtwcXGRn59fvn6bqyB9gT8rjv0VuJWjR4+qatWqat26tfz8/HTlyhU1adJEc+fOtXdpueKzkTfCTRnXoUMHRUREKCIiQhUqVJC3t7eioqKsPwkfGBioqVOnKjw8XF5eXho8eLAkaefOnWrXrp3c3Nzk7++v4cOH68qVK9blpqWlady4cfL395fFYlHdunW1cOFCSdlPNR07dkzdunVTpUqVVL58eTVq1EgbN27Msa8krV69Wo0aNZLFYlFgYKBmzJhhs02BgYGaNm2annnmGXl6eqpmzZrcgfoOcWN/HjlypLy9vRUWFqaDBw/qkUcekYeHh3x9ffX000/r7Nmz1nmysrL0j3/8Q3Xr1pXFYlHNmjX1yiuvWKePGzdO9evXl7u7u2rXrq2oqCj9/vvv9tg8lEL9+/fXCy+8oOTkZDk4OCgwMFCPPPKIXn75ZT322GP5Xo5hGJo0aZJq1qwpi8WiatWqafjw4dbpeX2nStL27dvVsmVLWSwWVa1aVePHj1dGRoZ1ek6fDUm3/HzcqQg3JrB06VKVK1dOe/bs0ezZszVz5ky999571unTp09XkyZNtG/fPkVFReno0aN6+OGH1bNnT3333Xdavny5du7cqYiICOs84eHh+te//qU333xThw4d0jvvvCMPD48c1z9s2DClpaXpiy++0IEDB/Taa6/l2nfv3r168skn9de//lUHDhzQpEmTFBUVpSVLltj0mzFjhlq0aKF9+/Zp6NChev7555WYmFj0Fwul3tKlS+Xi4qJdu3bp1Vdf1YMPPqhmzZrpm2++0aZNm5Samqonn3zS2n/ChAl69dVXFRUVpR9++EEfffSR9Q7nkuTp6aklS5bohx9+0OzZs7VgwQK98cYb9tg0lEKzZ8/WlClTVKNGDZ06dUpff/11oZazevVqvfHGG3rnnXd0+PBhrV27Vo0bN7ZOz+s79eTJk+rSpYvuu+8+JSQkaN68eVq4cKFefvllm3Xc/NmYP3++zp8/f8vPxx3LQJnWvn17o0GDBkZWVpa1bdy4cUaDBg0MwzCMgIAAo0ePHjbzPPvss8bgwYNt2nbs2GE4Ojoa165dMxITEw1JxpYtW3Jc57Zt2wxJxm+//WYYhmE0btzYmDRpUr76PvXUU8ZDDz1k02fMmDFGw4YNrc8DAgKMv/3tb9bnWVlZho+PjzFv3rw8XgmYQfv27Y1mzZpZn0+dOtXo3LmzTZ/jx48bkozExETj4sWLhsViMRYsWJDvdbz++utGcHCw9XlMTIzRpEkT6/N+/foZf/nLXwq9DSh73njjDSMgICDHaZKMNWvW3HIZM2bMMOrXr2+kp6dnm3ar79S///3vxt13323zPT537lzDw8PDyMzMNAwj+2fDMG79+biTceTGBO6//36bMS0hISE6fPiwMjMzJUktWrSw6Z+QkKAlS5bIw8PD+ggLC1NWVpaSkpK0f/9+OTk5qX379vla//Dhw/Xyyy+rTZs2iomJ0XfffZdr30OHDqlNmzY2bW3atLGpV5Luvfde678dHBzk5+en06dP56selG3BwcHWfyckJGjbtm02+2pQUJCkP8ZJHDp0SGlpaerUqVOuy1u+fLnatGkjPz8/eXh46KWXXlJycvJt3w6Y17Rp02z2yeTkZD3xxBO6du2aateurUGDBmnNmjXW00q3+k49dOiQQkJCbL7H27Rpo8uXL+vEiRPWtps/G9KtPx93MsLNHaB8+fI2zy9fvqznnntO+/fvtz4SEhJ0+PBh1alTR25ubgVa/sCBA/Xzzz/r6aef1oEDB9SiRQu99dZbRarZ2dnZ5rmDg4OysrKKtEyUDTfvr5cvX1a3bt1s9tX9+/fr8OHDeuCBB265r8bHx6tv377q0qWL1q9fr3379mnixIkMxkSRDBkyxGZ/rFatmvz9/ZWYmKi3335bbm5uGjp0qB544AH9/vvvBf5OzU1O3+V5fT7uZHb/VXAU3VdffWXz/Msvv1S9evXk5OSUY//mzZvrhx9+UN26dXOc3rhxY2VlZWn79u0KDQ3NVw3+/v4aMmSIhgwZogkTJmjBggV64YUXsvVr0KCBdu3aZdO2a9cu1a9fP9d6cedq3ry5Vq9ercDAQJUrl/3rql69enJzc9PWrVs1cODAbNN3796tgIAATZw40dp27Nix21ozzK9y5cqqXLlytnY3Nzd169ZN3bp107BhwxQUFKQDBw7c8ju1QYMGWr16tQzDsB692bVrlzw9PVWjRo1c67jV5+NOxpEbE0hOTlZkZKQSExP1r3/9S2+99ZZGjBiRa/9x48Zp9+7dioiIsKb8Tz75xDqgODAwUP369dMzzzyjtWvXKikpSXFxcVqxYkWOyxs5cqQ2b96spKQkffvtt9q2bZsaNGiQY98XX3xRW7du1dSpU/XTTz9p6dKlmjNnjkaPHl30FwKmM2zYMJ07d059+vTR119/raNHj2rz5s0aMGCAMjMz5erqqnHjxmns2LF6//33dfToUX355ZfWq1Dq1aun5ORkLVu2TEePHtWbb76pNWvW2HmrUNpdvnzZehREkvV0fV6nM5csWaKFCxfq4MGD+vnnn/Xhhx/Kzc1NAQEBt/xOHTp0qI4fP64XXnhBP/74oz755BPFxMQoMjJSjo65/5m+1efjTka4MYHw8HBdu3ZNLVu21LBhwzRixAjrJd85uffee7V9+3b99NNPateunZo1a6bo6GhVq1bN2mfevHnq1auXhg4dqqCgIA0aNMjmUvGbZWZmatiwYWrQoIEefvhh1a9fX2+//XaOfZs3b64VK1Zo2bJluueeexQdHa0pU6aof//+RXoNYE7VqlXTrl27lJmZqc6dO6tx48YaOXKkKlasaP3Sj4qK0osvvqjo6Gg1aNBAvXv3to7P6t69u0aNGqWIiAg1bdpUu3fvVlRUlD03CWXAN998o2bNmqlZs2aSpMjISOv3ZG4qVqyoBQsWqE2bNrr33nv12Wef6d///reqVKkiKe/v1OrVq2vjxo3as2ePmjRpoiFDhujZZ5/VSy+9lGed+fl83KkcDOP/b4iCMqlDhw5q2rSpZs2aZe9SAAAoFe7saAcAAEyHcAMAAEyF01IAAMBUOHIDAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABM5f8ApUph50bR8DwAAAAASUVORK5CYII=\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from sklearn.model_selection import StratifiedKFold, cross_val_score\n",
-        "from sklearn.tree import DecisionTreeClassifier\n",
-        "\n",
-        "dt = DecisionTreeClassifier(random_state=42)\n",
-        "\n",
-        "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
-        "\n",
-        "cv_scores = cross_val_score(\n",
-        "    estimator=dt,\n",
-        "    X=X_encoded,\n",
-        "    y=y,\n",
-        "    cv=kfold,\n",
-        "    scoring='accuracy'\n",
-        ")\n",
-        "\n",
-        "print(\"Hasil Cross Validation per fold:\", cv_scores)\n",
-        "print(\"Rata-rata CV Accuracy:\", cv_scores.mean())\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "H7FNZpqhlXRJ",
-        "outputId": "1639036f-c48c-47f0-cfb1-7a1ae90acc98"
-      },
-      "execution_count": 268,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Hasil Cross Validation per fold: [0.98      0.965     0.955     0.93      0.9798995]\n",
-            "Rata-rata CV Accuracy: 0.9619798994974875\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "plt.figure()\n",
-        "plt.plot(cv_scores, marker='o')\n",
-        "plt.title(\"Cross Validation Scores (Decision Tree)\")\n",
-        "plt.xlabel(\"Fold\")\n",
-        "plt.ylabel(\"Score\")\n",
-        "plt.ylim(0.9, 1.0)\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 472
-        },
-        "id": "vbOQrFNdxHjm",
-        "outputId": "482136fa-2b75-49e3-a70e-477e6c987fb5"
-      },
-      "execution_count": 269,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file