diff --git a/Project_Machine_Learning_Rata_Rata_Pendapatan_Bersih_Sebulan_Pekerja_Berusaha_Sendiri.ipynb b/Project_Machine_Learning_Rata_Rata_Pendapatan_Bersih_Sebulan_Pekerja_Berusaha_Sendiri.ipynb
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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Project Machine Learning**\n",
+        "Anggota\n",
+        "*   Fanysia Helena Kosuwandi (202310715031)\n",
+        "*   Faadhilah Zahraan Siregar (202310715184)\n"
+      ],
+      "metadata": {
+        "id": "19TRjzcGSKVV"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## **A. Business Understanding**\n",
+        "\n",
+        "1. **Latar Belakang**\n",
+        "\n",
+        "Dataset ini menyajikan rata-rata pendapatan bersih bulanan pekerja mandiri (berusaha sendiri) di 38 provinsi di Indonesia, yang dikategorikan berdasarkan tingkat pendidikan terakhir. Dalam konteks pembangunan wilayah, pendidikan sering kali dipandang sebagai faktor utama peningkatan kesejahteraan. Namun, hubungan antara jenjang pendidikan dan pendapatan sering kali bervariasi secara geografis akibat perbedaan struktur ekonomi daerah\n",
+        "\n",
+        "\n",
+        "---\n",
+        "\n",
+        "\n",
+        "2. **Permasalahan Bisnis**\n",
+        "\n",
+        "Pemerintah dan pemangku kebijakan sering kesulitan dalam memetakan provinsi mana yang memiliki profil kemakmuran serupa. Tanpa pemetaan yang jelas, intervensi ekonomi mungkin menjadi tidak tepat sasaran. Masalah utamanya adalah:\n",
+        "*   Bagaimana mengelompokkan provinsi-provinsi di Indonesia berdasarkan pola pendapatan lintas jenjang pendidikan?\n",
+        "*   Apakah terdapat wilayah yang menunjukkan kesenjangan ekstrem antara pekerja berpendidikan rendah dan tinggi?\n",
+        "\n",
+        "\n",
+        "\n",
+        "---\n",
+        "\n",
+        "\n",
+        "\n",
+        "3. **Tujuan Analisis**\n",
+        "\n",
+        "Proyek ini bertujuan untuk:\n",
+        "*  Segmentasi Wilayah: Mengelompokkan 38 provinsi ke dalam klaster ekonomi (Klaster Ekonomi Tinggi, Klaster Menengah - Rendah).\n",
+        "*   Identifikasi Pola: Memahami profil pendapatan pekerja mandiri, apakah kenaikan jenjang pendidikan di suatu wilayah berkorelasi linier dengan kenaikan pendapatan secara konsisten di seluruh Indonesia.\n",
+        "*   Dasar Rekomendasi: Memberikan gambaran bagi pembuat kebijakan untuk menentukan prioritas pembangunan ekonomi regional yang berbasis pada realitas pendapatan di lapangan.\n",
+        "\n",
+        "\n",
+        "\n",
+        "---\n",
+        "\n",
+        "4. **Metodologi**\n",
+        "\n",
+        "Analisis ini akan menggunakan pendekatan **Unsupervised Learning** dengan model **K-Means Clustering**. Model dibandingkan dengan **Hierarchical Clustering**. Model akan dievaluasi menggunakan metode **Elbow dan Silhouette Coefficient**, serta diuji stabilitasnya menggunakan **Bootstrap Resampling** untuk memastikan bahwa pengelompokan yang terbentuk bersifat valid"
+      ],
+      "metadata": {
+        "id": "KRZ8QoKJgF0o"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## **B. Import LIBRARY DAN DATA**"
+      ],
+      "metadata": {
+        "id": "gKmUh61cTfso"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "3dbDvMNIR7iz"
+      },
+      "outputs": [],
+      "source": [
+        "# import library\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from sklearn.cluster import KMeans, AgglomerativeClustering\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.metrics import silhouette_score, silhouette_samples, adjusted_rand_score\n",
+        "from scipy.cluster.hierarchy import dendrogram, linkage\n",
+        "from sklearn.utils import resample\n",
+        "from sklearn.decomposition import PCA"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Load dataset\n",
+        "df = pd.read_csv('Rata-Rata Pendapatan Bersih Sebulan Pekerja Berusaha Sendiri Menurut Provinsi dan Pendidikan yang Ditamatkan, 2025.csv')\n",
+        "\n",
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "1b2K1hghSPO6",
+        "outputId": "81bc387e-4242-4dff-9b54-603dea1c6f40"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         Provinsi  Tidak pernah sekolah/Belum tamat SD      SD     SMP  \\\n",
+              "0            ACEH                               1688.8  1708.2  1894.6   \n",
+              "1  SUMATERA UTARA                               1197.6  1685.0  2099.3   \n",
+              "2  SUMATERA BARAT                               1272.7  1591.5  1706.6   \n",
+              "3            RIAU                               1737.9  2107.5  2673.6   \n",
+              "4           JAMBI                               1471.3  2311.1  2061.3   \n",
+              "\n",
+              "   SMA ke atas  \n",
+              "0       2452.2  \n",
+              "1       1988.2  \n",
+              "2       2208.0  \n",
+              "3       2986.6  \n",
+              "4       2660.8  "
+            ],
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+              "      <th>Provinsi</th>\n",
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+              "      <td>1708.2</td>\n",
+              "      <td>1894.6</td>\n",
+              "      <td>2452.2</td>\n",
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+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>SUMATERA UTARA</td>\n",
+              "      <td>1197.6</td>\n",
+              "      <td>1685.0</td>\n",
+              "      <td>2099.3</td>\n",
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+              "      <td>2208.0</td>\n",
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+              "      <th>3</th>\n",
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+              "      <td>1737.9</td>\n",
+              "      <td>2107.5</td>\n",
+              "      <td>2673.6</td>\n",
+              "      <td>2986.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>JAMBI</td>\n",
+              "      <td>1471.3</td>\n",
+              "      <td>2311.1</td>\n",
+              "      <td>2061.3</td>\n",
+              "      <td>2660.8</td>\n",
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+              "\n",
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+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 38,\n  \"fields\": [\n    {\n      \"column\": \"Provinsi\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 38,\n        \"samples\": [\n          \"PAPUA BARAT DAYA\",\n          \"PAPUA TENGAH\",\n          \"JAMBI\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tidak pernah sekolah/Belum tamat SD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 344.0132995543491,\n        \"min\": 790.6,\n        \"max\": 2352.3,\n        \"num_unique_values\": 38,\n        \"samples\": [\n          1770.6,\n          1533.8,\n          1471.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 470.5880510347361,\n        \"min\": 913.9,\n        \"max\": 3135.2,\n        \"num_unique_values\": 38,\n        \"samples\": [\n          2452.6,\n          1971.4,\n          2311.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 539.9781660737917,\n        \"min\": 1249.3,\n        \"max\": 3840.5,\n        \"num_unique_values\": 38,\n        \"samples\": [\n          2524.1,\n          2830.8,\n          2061.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMA ke atas\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 464.11097724942243,\n        \"min\": 1494.3,\n        \"max\": 3526.3,\n        \"num_unique_values\": 38,\n        \"samples\": [\n          2836.8,\n          3127.4,\n          2660.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
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+      "source": [
+        "df.info()"
+      ],
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+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "o0D-M7PPTGbl",
+        "outputId": "3d9391bd-cf8f-4dc2-9f05-12f9513e52a5"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 38 entries, 0 to 37\n",
+            "Data columns (total 5 columns):\n",
+            " #   Column                               Non-Null Count  Dtype  \n",
+            "---  ------                               --------------  -----  \n",
+            " 0   Provinsi                             38 non-null     object \n",
+            " 1   Tidak pernah sekolah/Belum tamat SD  38 non-null     float64\n",
+            " 2   SD                                   38 non-null     float64\n",
+            " 3   SMP                                  38 non-null     float64\n",
+            " 4   SMA ke atas                          38 non-null     float64\n",
+            "dtypes: float64(4), object(1)\n",
+            "memory usage: 1.6+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## C. **PREPROCESSING DATA**"
+      ],
+      "metadata": {
+        "id": "t6zo8MCxTKxx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.isnull().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 241
+        },
+        "id": "QZmD0sRyfQ3_",
+        "outputId": "a97eef4c-5486-43f4-fdfc-fa5b44ebe5f9"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Provinsi                               0\n",
+              "Tidak pernah sekolah/Belum tamat SD    0\n",
+              "SD                                     0\n",
+              "SMP                                    0\n",
+              "SMA ke atas                            0\n",
+              "dtype: int64"
+            ],
+            "text/html": [
+              "<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>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Provinsi</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Tidak pernah sekolah/Belum tamat SD</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>SD</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>SMP</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>SMA ke atas</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# memastikan hanya kolom pendidikan yang digunakan sebagai fitur\n",
+        "features = ['Tidak pernah sekolah/Belum tamat SD', 'SD', 'SMP', 'SMA ke atas']\n",
+        "df_clean = df[['Provinsi'] + features].dropna()"
+      ],
+      "metadata": {
+        "id": "kz6BCVBYTKgZ"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Standardisasi Data\n",
+        "scaler = StandardScaler()\n",
+        "X_scaled = scaler.fit_transform(df_clean[features])"
+      ],
+      "metadata": {
+        "id": "qbuLskfOTWNb"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## **C. MODELING**"
+      ],
+      "metadata": {
+        "id": "uk7GJZ_yTbwE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### **K-means Clustering**"
+      ],
+      "metadata": {
+        "id": "jpGWovQeT0ej"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Menentukan jumlah klaster optimal\n",
+        "sil_scores = []\n",
+        "K = range(2, 6)\n",
+        "for k in K:\n",
+        "    km = KMeans(n_clusters=k, random_state=42, n_init=10)\n",
+        "    labels = km.fit_predict(X_scaled)\n",
+        "    sil_scores.append(silhouette_score(X_scaled, labels))"
+      ],
+      "metadata": {
+        "id": "mxeNBVOxTZTD"
+      },
+      "execution_count": 38,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "optimal_k = K[np.argmax(sil_scores)]\n",
+        "print(f\"\\nJumlah Klaster Optimal: {optimal_k}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TBveKhUPTp9d",
+        "outputId": "11711307-7446-4df6-f887-07b1514fc611"
+      },
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Jumlah Klaster Optimal: 2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Run K-Means\n",
+        "kmeans = KMeans(n_clusters=optimal_k, random_state=42, n_init=10)\n",
+        "df_clean['KMeans_Cluster'] = kmeans.fit_predict(X_scaled)"
+      ],
+      "metadata": {
+        "id": "VyVPl5nITquX"
+      },
+      "execution_count": 40,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install adjustText plotly\n",
+        "\n",
+        "import plotly.express as px\n",
+        "from adjustText import adjust_text\n",
+        "from sklearn.preprocessing import RobustScaler"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qsOp2HsTaGjS",
+        "outputId": "810e7fa1-a65c-4c56-f58e-62f2897a1031"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting adjustText\n",
+            "  Downloading adjustText-1.3.0-py3-none-any.whl.metadata (3.1 kB)\n",
+            "Requirement already satisfied: plotly in /usr/local/lib/python3.12/dist-packages (5.24.1)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from adjustText) (2.0.2)\n",
+            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (from adjustText) (3.10.0)\n",
+            "Requirement already satisfied: scipy in /usr/local/lib/python3.12/dist-packages (from adjustText) (1.16.3)\n",
+            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.12/dist-packages (from plotly) (9.1.2)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.12/dist-packages (from plotly) (25.0)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->adjustText) (1.3.3)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib->adjustText) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib->adjustText) (4.61.0)\n",
+            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->adjustText) (1.4.9)\n",
+            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib->adjustText) (11.3.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->adjustText) (3.2.5)\n",
+            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/dist-packages (from matplotlib->adjustText) (2.9.0.post0)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.7->matplotlib->adjustText) (1.17.0)\n",
+            "Downloading adjustText-1.3.0-py3-none-any.whl (13 kB)\n",
+            "Installing collected packages: adjustText\n",
+            "Successfully installed adjustText-1.3.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Gunakan RobustScaler agar outlier tidak menciutkan klaster lain\n",
+        "scaler_robust = RobustScaler()\n",
+        "X_robust = scaler_robust.fit_transform(df_clean[features])\n",
+        "\n",
+        "pca = PCA(n_components=2)\n",
+        "X_pca_robust = pca.fit_transform(X_robust)\n",
+        "\n",
+        "df_visual = pd.DataFrame(X_pca_robust, columns=['PC1', 'PC2'])\n",
+        "df_visual['Cluster'] = df_clean['KMeans_Cluster'].astype(str)\n",
+        "df_visual['Provinsi'] = df_clean['Provinsi'].values"
+      ],
+      "metadata": {
+        "id": "HDbl6umEa5AQ"
+      },
+      "execution_count": 21,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Visualisasi Klaster\n",
+        "fig = px.scatter(df_visual, x='PC1', y='PC2', color='Cluster',\n",
+        "                 text='Provinsi', title='Visualisasi Klaster',\n",
+        "                 labels={'PC1': 'Kesejahteraan Ekonomi', 'PC2': 'Variasi Pendidikan'},\n",
+        "                 hover_data=['Provinsi'])\n",
+        "\n",
+        "fig.update_traces(textposition='top center')\n",
+        "fig.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "YRVIYF0mbOFY",
+        "outputId": "7a8fa1d9-13c0-4944-fc7a-17522f60871d"
+      },
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
+              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.35.2.min.js\"></script>                <div id=\"66355bea-c6ee-4c54-9d58-556b17d7f332\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"66355bea-c6ee-4c54-9d58-556b17d7f332\")) {                    Plotly.newPlot(                        \"66355bea-c6ee-4c54-9d58-556b17d7f332\",                        [{\"customdata\":[[\"ACEH\"],[\"SUMATERA UTARA\"],[\"SUMATERA BARAT\"],[\"SUMATERA SELATAN\"],[\"LAMPUNG\"],[\"JAWA BARAT\"],[\"JAWA TENGAH\"],[\"DI YOGYAKARTA\"],[\"JAWA TIMUR\"],[\"NUSA TENGGARA BARAT\"],[\"NUSA TENGGARA TIMUR\"],[\"KALIMANTAN SELATAN\"],[\"SULAWESI SELATAN\"],[\"SULAWESI TENGGARA\"],[\"GORONTALO\"],[\"SULAWESI BARAT\"],[\"MALUKU\"],[\"MALUKU UTARA\"],[\"PAPUA BARAT\"]],\"hovertemplate\":\"Cluster=0\\u003cbr\\u003eKesejahteraan Ekonomi=%{x}\\u003cbr\\u003eVariasi Pendidikan=%{y}\\u003cbr\\u003eProvinsi=%{customdata[0]}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"0\",\"marker\":{\"color\":\"#636efa\",\"symbol\":\"circle\"},\"mode\":\"markers+text\",\"name\":\"0\",\"orientation\":\"v\",\"showlegend\":true,\"text\":[\"ACEH\",\"SUMATERA UTARA\",\"SUMATERA BARAT\",\"SUMATERA SELATAN\",\"LAMPUNG\",\"JAWA BARAT\",\"JAWA TENGAH\",\"DI YOGYAKARTA\",\"JAWA TIMUR\",\"NUSA TENGGARA BARAT\",\"NUSA TENGGARA TIMUR\",\"KALIMANTAN SELATAN\",\"SULAWESI SELATAN\",\"SULAWESI TENGGARA\",\"GORONTALO\",\"SULAWESI BARAT\",\"MALUKU\",\"MALUKU UTARA\",\"PAPUA BARAT\"],\"x\":[-0.29566323217866547,-1.1019777360098733,-1.2516833451631233,-0.07197089201825384,-0.26361230862990437,-0.5529357506172984,-2.3233432676588524,-3.177355744726937,-1.7973765931277341,-2.3132870583170586,-3.023043152200153,-0.2164765806805541,-0.2994525313837011,-0.6212060402562206,-0.8556219936060686,-1.9237455547099356,-0.6235398738676388,-0.4440339097436997,-1.1473466875379936],\"xaxis\":\"x\",\"y\":[-0.10670566792834468,0.08369060315914971,0.08685281750000426,-0.21512732888551916,-0.6724774360540877,0.04022145114590387,0.06840241306566085,0.5939271074005922,0.10383632197336744,0.10135070913892161,-0.30892923570107766,-0.04102195988193295,-0.29381129276612755,-0.18719472562813125,-1.215109234528576,0.010527434198427256,0.6305586230495869,-0.18481566709454803,-0.7617444319402553],\"yaxis\":\"y\",\"type\":\"scatter\",\"textposition\":\"top center\"},{\"customdata\":[[\"RIAU\"],[\"JAMBI\"],[\"BENGKULU\"],[\"KEP. 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Ekonomi\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Variasi Pendidikan\"}},\"legend\":{\"title\":{\"text\":\"Cluster\"},\"tracegroupgap\":0},\"title\":{\"text\":\"Visualisasi Klaster\"}},                        {\"responsive\": true}                    ).then(function(){\n",
+              "                            \n",
+              "var gd = document.getElementById('66355bea-c6ee-4c54-9d58-556b17d7f332');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#heatmap\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "sns.heatmap(analysis, annot=True, cmap='YlGnBu', fmt='.1f')\n",
+        "plt.title('Karakteristik Pendapatan Rata-rata per Klaster (Ribu Rupiah)')\n",
+        "plt.ylabel('Nomor Klaster')\n",
+        "plt.xlabel('Tingkat Pendidikan')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 536
+        },
+        "id": "6Ya6Qyx_ZXiN",
+        "outputId": "7521c964-a593-4aa7-e29f-01b9e6c1cd87"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### **Hierarchical Clustering (Agglomerative Clustering)**"
+      ],
+      "metadata": {
+        "id": "sPT0uunzT3U7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "agglo = AgglomerativeClustering(n_clusters=optimal_k)\n",
+        "df_clean['Agglo_Cluster'] = agglo.fit_predict(X_scaled)"
+      ],
+      "metadata": {
+        "id": "MMHe6V-2TuE9"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Visualisasi Dendrogram\n",
+        "plt.figure(figsize=(12, 6))\n",
+        "plt.title(\"Dendrogram Hierarchical Clustering\")\n",
+        "linkage_matrix = linkage(X_scaled, method='ward')\n",
+        "dendrogram(linkage_matrix, labels=df_clean['Provinsi'].values, leaf_rotation=90)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 523
+        },
+        "id": "Vi0nTNyQUM0Q",
+        "outputId": "286e8813-32b7-4eb1-c3d5-0767912ea9cc"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## **D. EVALUASI MODEL**\n"
+      ],
+      "metadata": {
+        "id": "nhVbKNGNUY_G"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "- ### **Silhouette analysis**"
+      ],
+      "metadata": {
+        "id": "8mk--wdXkMRP"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def plot_silhouette(X, labels, title):\n",
+        "    score = silhouette_score(X, labels)\n",
+        "    sample_values = silhouette_samples(X, labels)\n",
+        "\n",
+        "    plt.figure(figsize=(8, 4))\n",
+        "    y_lower = 10\n",
+        "    for i in range(np.max(labels) + 1):\n",
+        "        ith_cluster_values = sample_values[labels == i]\n",
+        "        ith_cluster_values.sort()\n",
+        "        size_cluster_i = ith_cluster_values.shape[0]\n",
+        "        y_upper = y_lower + size_cluster_i\n",
+        "        plt.fill_betweenx(np.arange(y_lower, y_upper), 0, ith_cluster_values)\n",
+        "        y_lower = y_upper + 10\n",
+        "\n",
+        "    plt.axvline(x=score, color=\"red\", linestyle=\"--\")\n",
+        "    plt.title(f\"{title} | Rata-rata Score: {score:.2f}\")\n",
+        "    plt.show()\n",
+        "\n",
+        "plot_silhouette(X_scaled, df_clean['KMeans_Cluster'], \"Evaluasi Silhouette K-Means\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 391
+        },
+        "id": "XVOpkipYUQyI",
+        "outputId": "3d527b05-3c6d-45c1-d5d9-a9762f063975"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### - **Elbow Method**"
+      ],
+      "metadata": {
+        "id": "eGh9gpYojPiE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.cluster import KMeans\n",
+        "\n",
+        "# Menghitung Inersia untuk berbagai nilai K\n",
+        "inertia = []\n",
+        "K = range(1, 10)\n",
+        "\n",
+        "for k in K:\n",
+        "    km = KMeans(n_clusters=k, random_state=42, n_init=10)\n",
+        "    km.fit(X_scaled)\n",
+        "    inertia.append(km.inertia_)\n",
+        "\n",
+        "# Plotting\n",
+        "plt.figure(figsize=(8, 5))\n",
+        "plt.plot(K, inertia, 'bx-')\n",
+        "plt.xlabel('Jumlah Klaster (k)')\n",
+        "plt.ylabel('Inersia (Within-cluster Sum of Squares)')\n",
+        "plt.title('Elbow Method untuk Menentukan K Optimal')\n",
+        "plt.axvline(x=3, color='red', linestyle='--')\n",
+        "plt.grid(True)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 487
+        },
+        "id": "RhVl8FaGeaEt",
+        "outputId": "2766cc65-c2bc-4b87-abe3-1e495aa58604"
+      },
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## **E. CROSS VALIDATION**\n",
+        "- **BOOTSTRAP RESAMPLING**"
+      ],
+      "metadata": {
+        "id": "r_pBnqaNUh5S"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ari_scores = []\n",
+        "n_iterations = 50\n",
+        "\n",
+        "for i in range(n_iterations):\n",
+        "    # Bootstrap sample\n",
+        "    X_bootstrap, ids = resample(X_scaled, range(len(X_scaled)), random_state=i)\n",
+        "\n",
+        "    # Fit model pada bootstrap data\n",
+        "    km_boot = KMeans(n_clusters=optimal_k, random_state=42, n_init=10)\n",
+        "    boot_labels = km_boot.fit_predict(X_bootstrap)"
+      ],
+      "metadata": {
+        "id": "S1uumRCoUife"
+      },
+      "execution_count": 35,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Fit model pada original data menggunakan indices yang sama\n",
+        "original_labels = df_clean['KMeans_Cluster'].iloc[ids].values\n",
+        "\n",
+        "# Hitung ARI (Semakin mendekati 1, semakin stabil klaster)\n",
+        "ari = adjusted_rand_score(original_labels, boot_labels)\n",
+        "ari_scores.append(ari)\n",
+        "\n",
+        "avg_ari = np.mean(ari_scores)\n",
+        "print(f\"Rata-rata Stability Score (ARI): {avg_ari:.2f}\")\n",
+        "\n",
+        "if avg_ari > 0.7:\n",
+        "    print(\"Kesimpulan: Klaster sangat STABIL.\")\n",
+        "elif avg_ari > 0.4:\n",
+        "    print(\"Kesimpulan: Klaster CUKUP STABIL.\")\n",
+        "else:\n",
+        "    print(\"Kesimpulan: Klaster TIDAK STABIL (Pola data terlalu acak/outlier ekstrem).\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MJVZ4U5BUojl",
+        "outputId": "04462478-b545-4ed4-d221-8220770d8175"
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Rata-rata Stability Score (ARI): 0.45\n",
+            "Kesimpulan: Klaster CUKUP STABIL.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## **Hasil**"
+      ],
+      "metadata": {
+        "id": "qO4blY6iUw5_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Melihat profil pendapatan per klaster\n",
+        "analysis = df_clean.groupby('KMeans_Cluster')[features].mean()\n",
+        "print(\"\\n--- Profil Pendapatan Rata-rata per Klaster (K-Means) ---\")\n",
+        "print(analysis)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KdfsQWdLUuUo",
+        "outputId": "0e5170ea-5956-4f91-bc6f-123af9842808"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "--- Profil Pendapatan Rata-rata per Klaster (K-Means) ---\n",
+            "                Tidak pernah sekolah/Belum tamat SD           SD          SMP  \\\n",
+            "KMeans_Cluster                                                                  \n",
+            "0                                       1343.721053  1619.163158  1777.026316   \n",
+            "1                                       1769.878947  2266.431579  2510.884211   \n",
+            "\n",
+            "                SMA ke atas  \n",
+            "KMeans_Cluster               \n",
+            "0               2083.610526  \n",
+            "1               2809.221053  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Menampilkan Anggota Klaster\n",
+        "for c in range(optimal_k):\n",
+        "    prov = df_clean[df_clean['KMeans_Cluster'] == c]['Provinsi'].tolist()\n",
+        "    print(f\"\\nKlaster {c} (Anggota): {', '.join(prov)}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6LmEPkbxUyxw",
+        "outputId": "2272a35c-f992-4447-8251-00deb98ca0d9"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Klaster 0 (Anggota): ACEH, SUMATERA UTARA, SUMATERA BARAT, SUMATERA SELATAN, LAMPUNG, JAWA BARAT, JAWA TENGAH, DI YOGYAKARTA, JAWA TIMUR, NUSA TENGGARA BARAT, NUSA TENGGARA TIMUR, KALIMANTAN SELATAN, SULAWESI SELATAN, SULAWESI TENGGARA, GORONTALO, SULAWESI BARAT, MALUKU, MALUKU UTARA, PAPUA BARAT\n",
+            "\n",
+            "Klaster 1 (Anggota): RIAU, JAMBI, BENGKULU, KEP. BANGKA BELITUNG, KEP. RIAU, DKI JAKARTA, BANTEN, BALI, KALIMANTAN BARAT, KALIMANTAN TENGAH, KALIMANTAN TIMUR, KALIMANTAN UTARA, SULAWESI UTARA, SULAWESI TENGAH, PAPUA BARAT DAYA, PAPUA, PAPUA SELATAN, PAPUA TENGAH, PAPUA PEGUNUNGAN\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## **Insight**"
+      ],
+      "metadata": {
+        "id": "3VOofzOEfY80"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Berdasarkan analisis klasterisasi K-Means dan Hierarchical Clustering, dengan jumlah klaster optimal 2, berikut adalah insight yang dapat ditarik:\n",
+        "\n",
+        "1. **Segmentasi Wilayah Ekonomi:**\n",
+        "- **Klaster 0 (Pendapatan Menengah-Rendah):** Klaster ini dicirikan oleh rata-rata pendapatan yang **lebih rendah di semua jenjang pendidikan** dibandingkan **Klaster 1**. Provinsi-provinsi dalam klaster ini meliputi: *ACEH, SUMATERA UTARA, SUMATERA BARAT, SUMATERA SELATAN, LAMPUNG, JAWA BARAT, JAWA TENGAH, DI YOGYAKARTA, JAWA TIMUR, NUSA TENGGARA BARAT, NUSA TENGGARA TIMUR, KALIMANTAN SELATAN, SULAWESI SELATAN, SULAWESI TENGGARA, GORONTALO, SULAWESI BARAT, MALUKU, MALUKU UTARA, PAPUA BARAT.*\n",
+        "- **Klaster 1 (Pendapatan Tinggi)**: Klaster ini menunjukkan rata-rata pendapatan yang secara signifikan **lebih tinggi di semua jenjang pendidikan**. Provinsi-provinsi yang termasuk dalam klaster ini adalah: *RIAU, JAMBI, BENGKULU, KEP. BANGKA BELITUNG, KEP. RIAU, DKI JAKARTA, BANTEN, BALI, KALIMANTAN BARAT, KALIMANTAN TENGAH, KALIMANTAN TIMUR, KALIMANTAN UTARA, SULAWESI UTARA, SULAWESI TENGAH, PAPUA BARAT DAYA, PAPUA, PAPUA SELATAN, PAPUA TENGAH, PAPUA PEGUNUNGAN.*\n",
+        "2. **Pola Pendapatan lintas Jenjang Pendidikan:**\n",
+        "Secara umum, di kedua klaster, terdapat korelasi positif antara jenjang pendidikan yang lebih tinggi dengan rata-rata pendapatan. Pekerja mandiri dengan pendidikan SMA ke atas cenderung memiliki pendapatan tertinggi, diikuti oleh SMP, SD, dan Tidak pernah sekolah/Belum tamat SD.\n",
+        "Perbedaan pendapatan antara Klaster 0 dan Klaster 1 terlihat konsisten di setiap jenjang pendidikan, menunjukkan bahwa faktor-faktor ekonomi regional memainkan peran besar dalam menentukan tingkat pendapatan secara keseluruhan, terlepas dari tingkat pendidikan.\n",
+        "3. **Stabilitas dan Kualitas Klaster:**\n",
+        "- Silhouette Score: Nilai silhouette score untuk K-Means adalah sekitar 0.38, yang menunjukkan bahwa klaster-klaster memiliki struktur yang cukup jelas namun ada beberapa tumpang tindih atau titik data yang berada di perbatasan klaster.\n",
+        "- Elbow Method: Metode Elbow menunjukkan bahwa 2 atau 3 klaster adalah pilihan yang wajar, mendukung pilihan optimal_k=2 yang ditemukan sebelumnya.\n",
+        "- Bootstrap Resampling (ARI): Nilai Adjusted Rand Index (ARI) sebesar 0.45 menunjukkan bahwa pengelompokan yang dihasilkan cukup stabil. Ini berarti bahwa jika analisis diulang dengan sampel data yang sedikit berbeda, hasil klasterisasinya cenderung serupa, memberikan kepercayaan pada temuan ini.\n",
+        "4. **Implikasi Kebijakan:**\n",
+        "- Targeted Interventions: Pemerintah dapat menggunakan klaster ini untuk merancang kebijakan ekonomi regional yang lebih tepat sasaran. Klaster 0 mungkin membutuhkan program peningkatan keterampilan atau subsidi yang berbeda dibandingkan Klaster 1.\n",
+        "- Evaluasi Kesenjangan: Analisis ini menyoroti wilayah-wilayah dengan profil pendapatan yang berbeda. Untuk Klaster 0, diperlukan investigasi lebih lanjut mengenai hambatan struktural yang menyebabkan pendapatan lebih rendah, meskipun telah mencapai tingkat pendidikan tertentu.\n",
+        "- Pembangunan Berbasis Pendidikan: Meskipun pendidikan berkorelasi positif dengan pendapatan, perbedaan antar klaster menunjukkan bahwa hanya meningkatkan pendidikan mungkin tidak cukup tanpa mengatasi isu-isu ekonomi makro di tingkat provinsi.\n",
+        "\n",
+        "Secara keseluruhan, proyek ini berhasil mengidentifikasi dua klaster provinsi dengan profil pendapatan yang berbeda, memberikan dasar yang kuat untuk rekomendasi kebijakan pembangunan ekonomi regional di Indonesia."
+      ],
+      "metadata": {
+        "id": "M0WPdvuTmArT"
+      }
+    }
+  ]
+}
\ No newline at end of file