From aa8d1595a42d27f2ff037aafcbebe84d3ae02775 Mon Sep 17 00:00:00 2001 From: 202310715065 ANANDA DWI PRASETYO <202310715065@mhs.ubharajaya.ac.id> Date: Sat, 13 Dec 2025 21:08:12 +0700 Subject: [PATCH] Upload files to "CLUSTERING" --- ...ERARCHICAL_CLUSTERING_KELOMPOK5_F5A4.ipynb | 309 +++++++++++ .../K_MEANS_CLUSTERING_KELOMPOK5_F5A4.ipynb | 514 ++++++++++++++++++ ...N_K_MEANS_VS_HIERARCHICAL_CLUSTERING.ipynb | 379 +++++++++++++ 3 files changed, 1202 insertions(+) create mode 100644 CLUSTERING/HIERARCHICAL_CLUSTERING_KELOMPOK5_F5A4.ipynb create mode 100644 CLUSTERING/K_MEANS_CLUSTERING_KELOMPOK5_F5A4.ipynb create mode 100644 CLUSTERING/PERBANDINGAN_K_MEANS_VS_HIERARCHICAL_CLUSTERING.ipynb diff --git a/CLUSTERING/HIERARCHICAL_CLUSTERING_KELOMPOK5_F5A4.ipynb b/CLUSTERING/HIERARCHICAL_CLUSTERING_KELOMPOK5_F5A4.ipynb new file mode 100644 index 0000000..8ff52fa --- /dev/null +++ b/CLUSTERING/HIERARCHICAL_CLUSTERING_KELOMPOK5_F5A4.ipynb @@ -0,0 +1,309 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "cQBJpfhL37Xb", + "outputId": "8fcae14d-bf39-4f29-87dd-e622f2dfc338" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "============================================================\n", + "EVALUASI HIERARCHICAL CLUSTERING\n", + "============================================================\n", + "Silhouette Score : 0.599\n", + "Davies-Bouldin Index : 0.460\n", + "\n", + "Ringkasan Statistik per Cluster:\n", + "\n", + "| cluster | wilayah | pendidik_avg | sekolah_avg | rasio_avg |\n", + "|:-----------------|:----------|:---------------|:--------------|:------------|\n", + "| Kepadatan Sedang | 9 | 8944.44 | 323.33 | 27.13 |\n", + "| Kepadatan Tinggi | 3 | 22666.7 | 800 | 28.28 |\n", + "| Kepadatan Rendah | 12 | 3500 | 156.67 | 21.95 |\n", + "\n", + "Wilayah dalam setiap Cluster:\n", + " • Cluster 0 (Kepadatan Sedang): DKI Jakarta, Sumatera Utara, Banten, Sulawesi Selatan, Bali, Lampung, Riau, Aceh, Sumatera Barat\n", + " • Cluster 1 (Kepadatan Tinggi): Jawa Barat, Jawa Tengah, Jawa Timur\n", + " • Cluster 2 (Kepadatan Rendah): Kalimantan Timur, Papua, Maluku, NTB, Sulawesi Utara, Kalimantan Selatan, Jambi, Bengkulu, NTT, Papua Barat, Gorontalo, Maluku Utara\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Hasil clustering disimpan ke: hasil_hierarchical_pendidik_sma_2024.csv\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "\n", + " async function download(id, filename, size) {\n", + " if (!google.colab.kernel.accessAllowed) {\n", + " return;\n", + " }\n", + " const div = document.createElement('div');\n", + " const label = document.createElement('label');\n", + " label.textContent = `Downloading \"${filename}\": `;\n", + " div.appendChild(label);\n", + " const progress = document.createElement('progress');\n", + " progress.max = size;\n", + " div.appendChild(progress);\n", + " document.body.appendChild(div);\n", + "\n", + " const buffers = [];\n", + " let downloaded = 0;\n", + "\n", + " const channel = await google.colab.kernel.comms.open(id);\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + "\n", + " for await (const message of channel.messages) {\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + " if (message.buffers) {\n", + " for (const buffer of message.buffers) {\n", + " buffers.push(buffer);\n", + " downloaded += buffer.byteLength;\n", + " progress.value = downloaded;\n", + " }\n", + " }\n", + " }\n", + " const blob = new Blob(buffers, {type: 'application/binary'});\n", + " const a = document.createElement('a');\n", + " a.href = window.URL.createObjectURL(blob);\n", + " a.download = filename;\n", + " div.appendChild(a);\n", + " a.click();\n", + " div.remove();\n", + " }\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "download(\"download_0e2fb71a-d1cc-496d-bc67-bd040be32639\", \"hasil_hierarchical_pendidik_sma_2024.csv\", 1243)" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "============================================================\n", + "ANALISIS HIERARCHICAL CLUSTERING SELESAI\n", + "============================================================\n" + ] + } + ], + "source": [ + "# Hierarchical Clustering: Jumlah Pendidik SMA 2024\n", + "# Analisis Pengelompokan Wilayah Berdasarkan Data Pendidik\n", + "\n", + "# ==========================================\n", + "# 1. IMPORT LIBRARY\n", + "# ==========================================\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.cluster import AgglomerativeClustering\n", + "from sklearn.metrics import silhouette_score, davies_bouldin_score\n", + "from scipy.cluster.hierarchy import dendrogram, linkage\n", + "import warnings\n", + "from google.colab import files\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "plt.style.use('seaborn-v0_8-darkgrid')\n", + "sns.set_palette(\"husl\")\n", + "\n", + "# ==========================================\n", + "# 2. LOAD DATA\n", + "# ==========================================\n", + "data_contoh = {\n", + " 'wilayah': ['DKI Jakarta', 'Jawa Barat', 'Jawa Tengah', 'Jawa Timur',\n", + " 'Sumatera Utara', 'Banten', 'Sulawesi Selatan', 'Kalimantan Timur',\n", + " 'Papua', 'Maluku', 'Bali', 'NTB', 'Lampung', 'Riau',\n", + " 'Sulawesi Utara', 'Kalimantan Selatan', 'Jambi', 'Bengkulu',\n", + " 'Aceh', 'Sumatera Barat', 'NTT', 'Papua Barat', 'Gorontalo', 'Maluku Utara'],\n", + " 'jumlah_pendidik': [15000, 25000, 20000, 23000, 12000, 10000, 8000, 5000,\n", + " 3000, 2500, 6000, 4500, 7000, 6500, 4000, 5500, 4200, 3500,\n", + " 8500, 7500, 3800, 2000, 1800, 2200],\n", + " 'jumlah_sekolah': [450, 850, 750, 800, 400, 350, 300, 200,\n", + " 150, 120, 250, 180, 280, 260, 170, 220, 190, 160,\n", + " 320, 300, 190, 100, 90, 110]\n", + "}\n", + "\n", + "df = pd.DataFrame(data_contoh)\n", + "df['rasio_guru_per_sekolah'] = df['jumlah_pendidik'] / df['jumlah_sekolah']\n", + "\n", + "# ==========================================\n", + "# 3. PERSIAPAN DATA\n", + "# ==========================================\n", + "features = ['jumlah_pendidik', 'jumlah_sekolah']\n", + "X = df[features].values\n", + "scaler = StandardScaler()\n", + "X_scaled = scaler.fit_transform(X)\n", + "\n", + "# ==========================================\n", + "# 4. DENDROGRAM (MENENTUKAN JUMLAH CLUSTER)\n", + "# ==========================================\n", + "plt.figure(figsize=(12, 6))\n", + "linked = linkage(X_scaled, method='ward')\n", + "dendrogram(linked,\n", + " labels=df['wilayah'].values,\n", + " orientation='top',\n", + " distance_sort='descending',\n", + " show_leaf_counts=True)\n", + "plt.title('Dendrogram Hierarchical Clustering')\n", + "plt.xlabel('Wilayah')\n", + "plt.ylabel('Jarak')\n", + "plt.xticks(rotation=90)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# ==========================================\n", + "# 5. IMPLEMENTASI HIERARCHICAL CLUSTERING\n", + "# ==========================================\n", + "optimal_cluster = 3\n", + "hc = AgglomerativeClustering(n_clusters=optimal_cluster, metric='euclidean', linkage='ward')\n", + "df['cluster'] = hc.fit_predict(X_scaled)\n", + "\n", + "# ==========================================\n", + "# 6. EVALUASI CLUSTERING\n", + "# ==========================================\n", + "s_score = silhouette_score(X_scaled, df['cluster'])\n", + "db_score = davies_bouldin_score(X_scaled, df['cluster'])\n", + "\n", + "print(\"=\"*60)\n", + "print(\"EVALUASI HIERARCHICAL CLUSTERING\")\n", + "print(\"=\"*60)\n", + "print(f\"Silhouette Score : {s_score:.3f}\")\n", + "print(f\"Davies-Bouldin Index : {db_score:.3f}\")\n", + "\n", + "# ==========================================\n", + "# 7. ANALISIS HASIL CLUSTERING\n", + "# ==========================================\n", + "cluster_labels = {}\n", + "avg_pendidik = df.groupby('cluster')['jumlah_pendidik'].mean()\n", + "\n", + "for i in range(optimal_cluster):\n", + " if avg_pendidik[i] > 15000:\n", + " cluster_labels[i] = 'Kepadatan Tinggi'\n", + " elif avg_pendidik[i] > 7000:\n", + " cluster_labels[i] = 'Kepadatan Sedang'\n", + " else:\n", + " cluster_labels[i] = 'Kepadatan Rendah'\n", + "\n", + "df['label_cluster'] = df['cluster'].map(cluster_labels)\n", + "\n", + "summary = df.groupby('cluster').agg(\n", + " wilayah=('wilayah', 'count'),\n", + " pendidik_avg=('jumlah_pendidik', 'mean'),\n", + " sekolah_avg=('jumlah_sekolah', 'mean'),\n", + " rasio_avg=('rasio_guru_per_sekolah', 'mean')\n", + ").round(2).rename(index=cluster_labels)\n", + "\n", + "print(\"\\nRingkasan Statistik per Cluster:\\n\")\n", + "print(summary.to_markdown(numalign=\"left\", stralign=\"left\"))\n", + "\n", + "print(\"\\nWilayah dalam setiap Cluster:\")\n", + "for i in range(optimal_cluster):\n", + " wilayah_list = df[df['cluster'] == i]['wilayah'].values\n", + " print(f\" • Cluster {i} ({cluster_labels[i]}): {', '.join(wilayah_list)}\")\n", + "\n", + "# ==========================================\n", + "# 8. VISUALISASI HASIL CLUSTERING\n", + "# ==========================================\n", + "plt.figure(figsize=(8, 6))\n", + "sns.scatterplot(data=df,\n", + " x='jumlah_sekolah',\n", + " y='jumlah_pendidik',\n", + " hue='label_cluster',\n", + " style='label_cluster',\n", + " s=150,\n", + " palette='viridis',\n", + " edgecolor='black')\n", + "plt.title('Hierarchical Clustering: Pendidik vs Sekolah')\n", + "plt.xlabel('Jumlah Sekolah')\n", + "plt.ylabel('Jumlah Pendidik')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# ==========================================\n", + "# 9. EXPORT HASIL\n", + "# ==========================================\n", + "hasil_df = df[['wilayah', 'jumlah_pendidik', 'jumlah_sekolah',\n", + " 'rasio_guru_per_sekolah', 'cluster', 'label_cluster']].sort_values('cluster')\n", + "\n", + "hasil_df.to_csv('hasil_hierarchical_pendidik_sma_2024.csv', index=False)\n", + "print(\"Hasil clustering disimpan ke: hasil_hierarchical_pendidik_sma_2024.csv\")\n", + "\n", + "try:\n", + " files.download('hasil_hierarchical_pendidik_sma_2024.csv')\n", + "except:\n", + " print(\"Auto-download hanya berfungsi di Google Colab\")\n", + "\n", + "print(\"=\"*60)\n", + "print(\"ANALISIS HIERARCHICAL CLUSTERING SELESAI\")\n", + "print(\"=\"*60)" + ] + } + ] +} \ No newline at end of file diff --git a/CLUSTERING/K_MEANS_CLUSTERING_KELOMPOK5_F5A4.ipynb b/CLUSTERING/K_MEANS_CLUSTERING_KELOMPOK5_F5A4.ipynb new file mode 100644 index 0000000..9cdde37 --- /dev/null +++ b/CLUSTERING/K_MEANS_CLUSTERING_KELOMPOK5_F5A4.ipynb @@ -0,0 +1,514 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "7FFKv8RmyMPM", + "outputId": "4044b459-a29a-4512-8f3f-5fa685e520e2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "============================================================\n", + "EKSPLORASI DATA AWAL\n", + "============================================================\n", + "\n", + "Jumlah data: 24 wilayah. Tidak ada missing value.\n", + "\n", + "| wilayah | jumlah_pendidik | jumlah_sekolah | rasio_guru_per_sekolah |\n", + "|:---------------|:------------------|:-----------------|:-------------------------|\n", + "| DKI Jakarta | 15000 | 450 | 33.3333 |\n", + "| Jawa Barat | 25000 | 850 | 29.4118 |\n", + "| Jawa Tengah | 20000 | 750 | 26.6667 |\n", + "| Jawa Timur | 23000 | 800 | 28.75 |\n", + "| Sumatera Utara | 12000 | 400 | 30 |\n", + "\n", + "Statistik Deskriptif:\n", + " | | jumlah_pendidik | jumlah_sekolah | rasio_guru_per_sekolah |\n", + "|:------|:------------------|:-----------------|:-------------------------|\n", + "| count | 24 | 24 | 24 |\n", + "| mean | 7937.5 | 299.6 | 24.7 |\n", + "| std | 6573.4 | 214.8 | 3.7 |\n", + "| min | 1800 | 90 | 20 |\n", + "| 25% | 3725 | 167.5 | 21.6 |\n", + "| 50% | 5750 | 235 | 25 |\n", + "| 75% | 8875 | 327.5 | 26.7 |\n", + "| max | 25000 | 850 | 33.3 |\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "============================================================\n", + "PERSIAPAN DATA\n", + "============================================================\n", + "\n", + "Fitur yang digunakan: ['jumlah_pendidik', 'jumlah_sekolah']\n", + "Shape data: (24, 2)\n", + "Data telah dinormalisasi menggunakan StandardScaler.\n", + "\n", + "============================================================\n", + "MENENTUKAN JUMLAH CLUSTER OPTIMAL\n", + "============================================================\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Evaluasi untuk berbagai K:\n", + " | K | Inertia | Silhouette Score |\n", + "|----:|----------:|-------------------:|\n", + "| 2 | 10.4674 | 0.731571 |\n", + "| 3 | 3.67937 | 0.594546 |\n", + "| 4 | 1.82605 | 0.585692 |\n", + "| 5 | 1.17329 | 0.557479 |\n", + "| 6 | 0.830915 | 0.501176 |\n", + "| 7 | 0.532124 | 0.472502 |\n", + "| 8 | 0.399343 | 0.420903 |\n", + "| 9 | 0.322782 | 0.386624 |\n", + "| 10 | 0.249896 | 0.353665 |\n", + "\n", + "============================================================\n", + "IMPLEMENTASI K-MEANS CLUSTERING (K=3)\n", + "============================================================\n", + "\n", + "Clustering selesai! Iterasi: 6\n", + "Inertia: 3.68 | Silhouette Score: 0.595 | Davies-Bouldin Index: 0.464\n", + "\n", + "============================================================\n", + "ANALISIS HASIL CLUSTERING\n", + "============================================================\n", + "\n", + "Ringkasan Statistik per Cluster:\n", + " | cluster | wilayah | pendidik_avg | sekolah_avg | rasio_avg |\n", + "|:-----------------|:----------|:---------------|:--------------|:------------|\n", + "| Kepadatan Rendah | 14 | 3892.86 | 170.71 | 22.31 |\n", + "| Kepadatan Tinggi | 3 | 22666.7 | 800 | 28.28 |\n", + "| Kepadatan Sedang | 7 | 9714.29 | 342.86 | 27.88 |\n", + "\n", + "Wilayah dalam setiap Cluster:\n", + " • Cluster 0 (Kepadatan Rendah): Kalimantan Timur, Papua, Maluku, Bali, NTB, Riau, Sulawesi Utara, Kalimantan Selatan, Jambi, Bengkulu, NTT, Papua Barat, Gorontalo, Maluku Utara\n", + " • Cluster 1 (Kepadatan Tinggi): Jawa Barat, Jawa Tengah, Jawa Timur\n", + " • Cluster 2 (Kepadatan Sedang): DKI Jakarta, Sumatera Utara, Banten, Sulawesi Selatan, Lampung, Aceh, Sumatera Barat\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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AACgy2rdvr6efflobNmxQ8+bNNXXqVPXv31+HDh2Sm5ubtcsDgOw5Ocrk6mLtKgCr4NZrAPKLjbULAAAAAICcmjlzpjp37ix7e3tNnz5dcXFxmjRpkoKDgzV79mxrlwcAAADAwrhSAwAAAECRYWdnp9GjR0uSKlSooI8//tjKFQEAAAAoSIQaAAAAAAq1oKCgHPcdMGCABSsBAAAAYG2EGgAAAAAKteXLl+eon8FgINQAAAAAijlCDQAAAACFWnBwsLVLAAAAAFBIEGoAAAAAKFJOnDihr776SmFhYZKkGjVqqFu3bqpWrZqVKwMAAABgaTbWLgAAAAAAcuqLL75Q7969tW/fPiUmJioxMVFff/21unXrpgMHDli7PAAAAAAWxpUaAAAAAIqMd999V6+//roee+yxdMe3bNmi119/XZ9++qmVKgMAAABQELhSAwAAAECR8c8//6hLly4Zjvfs2VOnT5+2QkUAAAAAClK+hhpJSUn5OR0AAAAApFO5cmUdOnQow/HQ0FCVK1fOChUBAAAAKEi5vv3U1KlTNXfuXBmNxnTHjx49qsmTJ2vHjh35VhwAAAAA3G7IkCEaPny4evToodq1a0uSTp8+rR07dmjEiBFWrg4AAACApeU61Dh79qyee+45vf3223J0dJTJZNLSpUu1bNkyPfnkk5aoEQAAAAAkSQMHDpSnp6c2b96sgwcPKjExUdWrV9err76aYZ8NAAAAAMVPrkONNWvWaObMmRo0aJCmTZumxYsX69q1a1q5cqWaN29uiRoBAAAAQJIUGxurjh07qmPHjhnazp49qxo1alihKgAAAAAFJdd7apQqVUoLFixQr169NGzYMJUrV07bt28n0AAAAABgcU8++aQuX76c4fj69evVq1evgi8IAAAAQIHK0ZUaQUFBGY45OjrK19dXv//+u7Zt2yYbm5v5yIABA/K3QgAAAAC4pXXr1urXr58CAwNVr149Xbp0SVOnTtXx48cVEBBg7fIAAAAAWFiOQo3ly5dn2Va6dGl98MEHkiSDwUCoAQAAAMBiXnzxRdWpU0d+fn4aOHCg1q9fr1atWmnHjh1yc3PL1VxhYWGaP3++Dhw4IKPRqHbt2mnatGlydnbWTz/9pEWLFunUqVNydHRUp06d9OKLL6pMmTIWOjMAAAAAOZGjUCM4ONjSdQAAAABAjvTu3Vs1a9bUmDFj1KlTJy1YsCBP8zz//PPy9vZWcHCwoqOjNXr0aC1cuFATJ07UqFGjNHnyZPXv31/h4eEaPny43n77bU2ZMiWfzwYAAABAbuQo1Pjpp5/UqlUrSdIPP/yQZT+DwaDWrVvnT2UAAAAAIGnixImZHq9evbp27NihuLg4GY1GSdKbb76ZozmjoqLk7e2tiRMnysHBQQ4ODurdu7fWrl2r06dP68aNG+rTp49sbW1VsWJFtWvXTocPH863cwIAAACQNzkKNUaMGKE//vhDkjRs2LAs+xkMBh05ciR/KgMAAAAASXZ2dpker1mzpmrWrJmnOZ2dnTNc4XHhwgV5enqqfv368vT01Mcff6ynnnpKV65c0b59+/TEE09kO6fBkKdS7spS8wJFkcFQNH8mimLNgKUU1Z9jAIVHjkKNtEBDko4ePWqxYgAAAADgTnm9vVRuhISEaN26dVq6dKkcHBz03nvvafjw4Vq4cKEkqVu3bnrmmWeyHO/u7iCj0cYitYWHO1hkXqAocnV1kIeHk7XLyDV+joF/FdWfYwCFR45Cjf379+doMoPBoGbNmt1TQQAAAACQnc8//1zbtm3T5cuX9dlnnykxMVFr167V0KFDZcjDWz9//fVXjRw5UhMnTpSPj4+uXbumUaNGadSoUerbt6/Cw8P14osv6rXXXtPUqVMznePatViLves0MjLWMhMDRVBkZKzCw6OtXUau8XMM/Kuo/hwDKBg5CT1zFGoMHjw43WODwSCTyZThmNFo5D6zAAAAACzm/fffV1BQkAYMGKBly5ZJurk/xmeffabo6GiNHz8+V/MFBwdr8uTJmjFjhnr16iVJ2rVrlxwcHDRkyBBJN/fuGDZsmF588cUsQw1JuuMlUr6x1LxAUWQyFc2fiaJYM2ApRfXnGEDhkevbTwUHB+vLL7/Uc889p5o1a8pkMunEiRNauXKl+vTpY7FCAQDFi8lkUnR0lK5fv65SpUrJzc1d9vb21i4LAFDIBQUFacWKFapTp46WL18uSfLw8ND777+vIUOG5CrUOHjwoKZMmaIlS5aoTZs25uOpqalKTU1N1zcxMTFPV4EAAAAAyF85uumrnZ2d+WPRokWaPXu2vLy85ODgIEdHRzVp0kSzZ8/W/PnzLV0vAKCIi4mJ1qpVH6h1m4d1//3V1LSptxo1qqcaNSrK79mn9f33+zJcDQgAQJro6GjVqVMnw3FPT09du3Ytx/MkJydr+vTpmjRpUrpAQ5LatGmjixcv6uOPP1ZiYqIuXryoNWvW6JFHHrnn+gEAAADcm1zvZBcREaGEhIQMx1NTUxUZGZkfNQEAiiGTyaS3314kL686mvbyFF2PL6sHWgyRd7uR8mrznGo26qkffjqoJ57ooVY+zfTbb79au2QAQCFUt25dbd++PcPxVatWqXbt2jme5/fff9epU6c0d+5cNWzYMN2HnZ2dli1bpq1bt6ply5bq37+/6tWrp+nTp+fnqQAAAADIgxzdfup2bdu21bPPPqtBgwapatWqSk5O1sWLFxUUFKTWrVtbokYAQBFnMpn0wsSx+nj9R6pSt4Mq1W4ru9KO6fq4etZVxVqtFBNxXudCP1fPxx/TR2s+VocOnaxUNQCgMBo3bpxGjx6tjz/+WElJSRo5cqSOHz+u69ev6/3338/xPM2aNdOxY8eybK9SpQqvbwAAAIBCKNehxrx587R06VKtX79eFy9eVGJiojw9PdWuXTtNmjTJEjUCAIq4116bo4/Xr1Wd5k+qfNUHs+xnMBjk5F5d9X2e04kDH+sZvyf1+c6v1bBh44IrFgBQqLVq1Uq7du3Szp07Va9ePZUuXVpt2rRRt27d5Orqau3yAAAAAFhYrkONMmXK6IUXXtALL7xgiXoAAMXM6dOntHhxgGo27J5toHE7G6Ot6jQbpNAflmnKS5P0xeffWLZIAECRUqFCBfn7+0uSbty4oYSEBAINAEWCISbG2iUAVsP3P4D8kqNQY9OmTerbt68kKSgoKNu+AwYMyFUB33//vaZMmaIWLVpo8eLF5uNbtmzRtGnTVKpUqXT9169fr0aNGik1NVVLlizRzp07FRUVpUaNGmnWrFmqVq2aJCkyMlKzZs3SL7/8IhsbG/n6+mrGjBkqXbq0JOnIkSOaN2+ejhw5onLlymngwIEaOnRormoHANzdmjWrVLqssyrVzt0tPGyMpVS5Tkcd+Hm1/vwzVA0aeFmoQgBAUZCYmKh58+apS5cu8vHxkSQFBgbq7bffVkpKipo0aaKlS5fKxcXFypUCQEbOzi4qZWcnHTxk7VIAqyplZydnZ35XA7g3OQo1VqxYYQ41li9fnmU/g8GQq1Djgw8+0KZNm1SjRo1M25s3b661a9dm2rZ+/Xrt2LFDH3zwgSpUqKDFixdr9OjR2rZtmwwGg2bMmKHExETt3LlTSUlJGjdunAICAjR9+nTFx8drxIgR6t+/vwIDA/XXX39p6NChqlq1qjp37pzj+gEA2YuLi9O69R/Jo1pz2djk+uJAuVeqr7KO7lq9eoVef33x3QcAAIqtJUuWaP/+/XryySclSadOndLixYs1btw4tWnTRu+8847efvttzZgxw8qVAkBGnp4VFLh8taKirlu7lBLr/PlzCghYoEmTpqpaterWLqfEcnZ2kadnBWuXAaCIy9FfmL788kvz18HBwfn25Pb29tq0aZPmzZunhISEXI0NCgqSn5+fateuLUmaMGGCWrRooUOHDqlq1aravXu3tm7dKnd3d0nSqFGjNG7cOE2ZMkV79+41bypoNBrl5eWlfv36KSgoiFADAPLR/v3/U3TUddVp2TRP4w0GG7lXaaLPv/icUAMASrgvvvhCy5YtU7169SRJu3bt0v3336/nn39ekjRt2jQ9++yzhBoACi1Pzwr8MbcQqFatuu6/v661ywAA3IMchRqfffZZjifs1atXjvsOGTIk2/YLFy7o2Wef1eHDh+Xs7KyxY8fq8ccfV3x8vE6ePKkGDRqY+zo6OqpGjRoKCQlRdHS0jEaj+QWPJHl5eenGjRs6ffq0QkNDVa9ePRmNRnN7gwYNtHHjxixrMRhyfFpWkVZfYa+zKGJtLYe1tYzCtK7Xrl2VJNmXccvzHPZlXHTp9LVCcT6FaW2LG9bWclhby2FtC9bVq1fT/fv+559/Vps2bcyPa9SooatXr1qjNAAAAAAFKEehRkBAQLrHUVFRSkpKkrOzs0wmk6KiolS6dGlVqFAhV6FGdtzd3VWzZk298MILuv/++/XNN9/oxRdflKenp+677z6ZTKYM98t1cXFRRESEXF1d5ejoKMNtrzDT+kZERCgyMlLOzs7pxrq6uioyMlKpqamysbG5oxYHGY3pjxVW5co5WbuEYou1tRzW1jIKw7o6Otrf/OJe/uB36//lHh7WP580hWFtiyvW1nJYW8thbQuGg4ODYmNj5eDgoLi4OIWEhJg3C5dubhietn8eAAAAgOIrR6HGDz/8YP5648aNCg0N1bhx4+TmdvOdt5cvX9Zbb72lJk2a5Fth7du3V/v27c2Pu3Xrpm+++UZbtmzRpEmTJEkmkynL8dm1ZcWQxdvsrl2LLfTvwDMYbr6gvno1Wnk4dWSDtbUc1tYyCtO62tqWkSQlxkerdNm8Xa2RGB8lZ2cXhYdH52dpeVKY1ra4YW0th7W1HNY29+4loPb29tbmzZs1ZMgQrVmzRra2tmrVqpW5fc+ePapVq1Z+lAkAAACgEMv1rq3vvvuuvvrqq3TvgvL09NS0adP02GOPqV+/fvla4O2qVKmiw4cPy9XVVTY2NoqMjEzXHhkZqXLlysnd3V0xMTFKSUkx32IqrW9a+5kzZzKMTZs3M0XlharJVHRqLWpYW8thbS2jMKxrs2YPq0yZsrpy7qCqPdAp1+NNplRdCzuk7l07W/1cblcY1ra4Ym0th7W1HNa2YIwePVpDhw7V4sWLFR8fr6lTp5pfk3z22WeaPXu2Xn31VStXCQAAAMDScn1Ppfj4eF24cCHD8atXr+Z6s+/sbNiwQV988UW6Y6dOnVK1atVkb2+vOnXqKDQ01NwWFRWlc+fOqVGjRqpfv75MJpOOHj1qbg8JCZGzs7Nq1aolb29vHTt2TMnJyenaGzdunG/1AwAkR0cnDRz4pMLP/SKTKTXX469fOanYqMsaOnSYBaoDABQlDz74oHbs2KF58+Zpy5Yt6fbnS0lJ0csvv6wePXpYsUIAAAAABSHXV2p0795dgwcPVo8ePVS1alWlpKTowoUL+vzzz9WlS5d8KywxMVFz5sxRtWrV9MADD+irr77Sd999p08//VSSNGjQIAUGBqpdu3aqUKGCAgICVL9+fTVs2FCS1KVLF7311ltauHChEhMT9d5776lv376ytbWVr6+vHB0dtXTpUg0bNkzHjx/Xpk2b9MYbb+Rb/QCAm/z8hunDD1fo8tkDqlDz4RyPM6WmKOz4HjVs+KCaNGlqwQoBAEVFlSpVVKVKlQzHn3jiCStUAwAAAMAach1qTJs2TXXr1tXu3bv1ww8/KDExUZ6enho8eLD8/PxyNVdaAJF2xcTu3bsl3bxqYsiQIYqNjdW4ceN05coVVa1aVe+99568vb0lSQMHDtSVK1c0ePBgxcbGqkWLFnr33XfNc8+ePVuvvPKKOnXqpFKlSql79+6aMGGCJMnOzk7Lli3TK6+8osDAQHl4eGjChAnp9vAAAOSP+vUb6Jln/LVu3RrZl3GVa4W6dx1jMqXq9O9bFRNxVnM/2F4AVQIAAAAAAKAoyHWoYTQaNWDAAA0YMOCenzwkJCTLNoPBoFGjRmnUqFFZto8dO1Zjx47NtN3JyUmLFi3Kcv66detqw4YNuSsYAJAnCxa8oStXLuvLrz5UDa/HVKFmCxlt7TLtGxcTrrOHdyry0jEtXfqBWrVqXcDVAgAAAAAAoLDKdagRHR2tTz/9VKdOncp0D40333wzXwoDABQftra2WrnyI82aNV0rVixX2LHdKlf1IZWr0lCl7BxkSk1RXGy4rpzdr2sXj6qcR3lt2LBJ7dt3tHbpAAAAAAAAKERyHWpMmDBBx44dU9OmTVWmTBlL1AQAKIaMRqPmzFmg0aPHau3a1Vq9epUOn/ohXZ+HW7TSsNkf6rHHesjOLvMrOQAAJdsXX3yhxx57zNplAAAAALCSXIcav/76q7788ktVqFDBEvUAAIq5ihUrafLkqZowYbLOnTur69cjVaqUncqXL68KFSpauzwAQCH36quvqn379ipbtqy1SwEAAABgBbkONSpWrCgHBwdL1AIAKEFsbW113321rV0GAKCIGT9+vKZPn65evXqpcuXKMhqN6dpr1aplpcoAAAAAFIRchxrTpk3TvHnzNGzYMFWtWlUGgyFdO7cLAQAAAGApr776qqSbt6FKYzAYZDKZZDAYdOTIEWuVBgAAAKAA5GlPjbi4OH322WeZtvMiAgAAAIClfPvtt9YuAQAAAIAV5TrUeP/99y1RBwAAAADcVZUqVSRJV69e1YULF+Tt7W3ligAAAAAUpFyHGg8//LD564iICLm5ueVrQQAAAACQlUuXLmnatGn6v//7P9na2urw4cO6fPmy/P399f7776tatWrWLhEAAACABdnkdkBsbKxmzpypBx98UG3btpUkRUZGasSIEbp27Vq+FwgAAAAAaWbPni13d3ft2bNHNjY3X864u7urTZs2mjt3rpWrAwAAAGBpuQ41Zs+erfPnz2vFihXmFxGlSpWSo6MjLyIAAAAAWNTPP/+sWbNmqVKlSjIYDJIkW1tbjRs3Tr///rt1iwMAAABgcbm+/dTevXu1a9cuubu7m19EODg46JVXXlGXLl3yvUAAAAAASFOmTBmZTKYMx69fv66UlBQrVAQAAACgIOX6Sg2DwSBHR8cMx1NSUpSQkJAvRQEAAABAZlq2bKlp06bpr7/+kiRFRUXpl19+0X//+1+1b9/eusUBAAAAsLhchxpNmjTR66+/rvj4ePOxsLAwvfzyy+k2EQcAAACA/DZjxgwlJiaqa9euSkhIUIsWLeTn56fq1atrxowZ1i4PAAAAgIXl+vZTM2bM0KhRo9SsWTMlJyeradOmunHjhpo0aaI333zTEjUCAAAAgCTJxcVFy5Yt07Vr13T+/HnZ29uratWqmV5NDgAAAKD4yXWoUblyZX322Wf6448/9Pfff8ve3l7Vq1dXnTp1LFEfAAAAAKQTGRmpH3/8UZcvX5bBYFDFihXVunVrOTs7W7s0AAAAABaWq1DDZDLp9OnTMhqNatSokRo1amSpugAAAAAgg927d2v8+PFydHRUpUqVZDKZdOHCBcXFxentt99mXw0AAACgmMtxqBEWFqbnn39eJ06ckCR5e3vr/fffl6enp8WKAwAAAIDbzZs3T1OnTtWTTz4pg8Eg6eabr9auXatZs2Zp79691i0QAAAAgEXleKPw119/XXXr1tW+ffsUHBysatWq6fXXX7dkbQAAAACQTmRkpAYMGGAONCTJYDBo0KBBun79uhUrAwAAAFAQchxq/Prrr3r55ZdVoUIFVa5cWS+//LJ++eUXS9YGAAAAAOm0b99eP/74Y4bjBw4ckK+vrxUqAgAAAFCQcnz7qejoaLm7u5sfe3h48E4oAAAAAAWqevXqmjx5spo0aaJatWopJSVF586d02+//aaePXtq0aJF5r4vvPCCFSsFAAAAYAm52igcAAAAAKzp4MGDqlu3rmJjY3X48GHz8bp16+ro0aPmx7ffnqq4SEmMsXYJgNXw/V84XLjwj2Jji+Z/i/Pnz6X7XBQ5ODiqUqXK1i4DAKwux6GGyWTSmTNnZDKZsj1Wq1at/K0QAAAAAG5Zu3attUuwmrjw36xdAoAS7Pr16xo+/BmlpqZau5R7EhCwwNol5JmNjY3WrdskFxcXa5cCAFaV41AjMTFRXbt2TRdgSNJ//vMfGQwGmUwmGQwGHTlyJN+LBAAAAICSroxHExntHK1dBmAVKYkxBHtW5uLiosDANUX2So3iwMHBkUADAJSLUOPbb7+1ZB0AAAAAgGwY7Rxla88fswBYD7c+AgAUBjkONapUqWLJOgAAAAAAAAAAALJlY+0CAAAAAAAAAAAAciLHV2oAAAAAgLW9++672baPGTOmgCoBAAAAYA2EGgAAAACKjO+//z7d45SUFIWFhUmSmjRpYo2SAAAAABQgQg0AAAAARUZQUFCGY6mpqVq2bJns7OysUBEAAACAgpTrUGP//v2aO3eu/vrrLyUlJWVoP3LkSL4UBgAAAAA5YWNjo+eee06+vr4aNmyYtcsBAAAAYEG5DjWmTJmiZs2a6b///a/s7e0tURMAAAAA5Mr+/fuVnJxs7TIAAAAAWFiuQ42IiAjNnTuXS7sBAAAAFLg2bdpkOBYfH6/Y2Fj5+fkVfEEAAAAAClSuQ422bdvqxIkT8vLyskQ9AAAAAJCliRMnZjhmb2+vGjVq8BoFAAAAKAFyFGrcvhlf48aNNXnyZHXs2FFVq1aVwWBI13fAgAH5WyEAAAAA3BIeHq7nnnvO2mUAAAAAsJIchRrLly/PcOyLL77IcMxgMBBqAAAAALCYNWvW6IknnpC7u7u1SwEAAABgBTkKNYKDg3M02dWrV++pGAAAAADIzrBhwzRu3Dg99thjqly5soxGY7r2zPbcAAAAAFB85HpPjTSpqalKTk42P7506ZKeeOIJ/fLLL/lSGAAg50wmk/73v5/08cdrdebsGSXEx6usg4O8Gnhp8OBnVa/eA9YuEQCAfPHaa69Jkvbv35+hzWAw6MiRIwVdEgAAAIAClOtQ48SJE5oyZYqOHz+ulJSUdG2NGjXKt8IAAHdnMpm0fv1HWrrsPZ04flROrpVVxrmybGztlBIeo4O/f6LAwKVq2bK1xo9/QR07PmrtkgEAuCdHjx7Nt7nCwsI0f/58HThwQEajUe3atdO0adPk7OyspKQkvfHGG9q2bZuSkpLUpk0bzZ49W66urvn2/AAAAAByzya3A1599VV5eXlp2bJlMhqNWrVqlSZOnKhWrVopMDDQEjUCADKRlJSkMWNGaOLEsYqMK62G7UapYYfxqtNsgGo/2Ed1mw9Sk0em6IGWz+jYX5c1cOATevvtxdYuGwCAQuP555+Xs7OzgoODtWXLFp04cUILFy6UJC1atEiHDx/W9u3btXv3bhmNRn366adWrhgAAABArq/UOHr0qFavXi1bW1vZ2NioVatWatWqlerVq6eZM2dqyZIllqgTAHAbk8mk8eNHa8vWzar38GCVq9Iw034GG6PKVfaWeyUvhR3fo7lzX5G9vZ1GjBhdwBUDAJA/HnjgARkMhizbc3r7qaioKHl7e2vixIlycHCQg4ODevfurbVr1yo+Pl4bNmzQ+vXrVaFCBUnS4sW8MQAAAAAoDHIdapQuXVpxcXFycnJS2bJldfnyZXl6eqpVq1YaP368BUoEANzpk0/Wa9OmoGwDjdsZDAZVrddRqanJmjlzmlq29FHjxk0KoFIAAPLXBx98kO5xamqqzp49q507d2rYsGE5nsfZ2VkLFixId+zChQvy9PRUaGiokpOTdeLECY0dO1Y3btxQp06dNG3aNJUtWzbLObPJWu6JpeYFiiKDgZ8JAABKulyHGu3bt9fTTz+tDRs2qHnz5po6dar69++vQ4cOyc3NzRI1AgBuYzKZtGzZ+/Ko0ihHgcbtqj3wqCL+OaQVKwL1zjtLLVQhAACW07Zt20yP+/r66qWXXlLnzp3zNG9ISIjWrVunpUuX6tKlS5Kk77//Xps3b9bVq1c1cuRILV68WC+//HKm493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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "============================================================\n", + "INTERPRETASI & REKOMENDASI\n", + "============================================================\n", + "\n", + "Cluster: 0 - Kepadatan Rendah (Rasio rata-rata: 22.31)\n", + " REKOMENDASI: **PENAMBAHAN guru** (Rasio di bawah standar ideal 25-30)\n", + "\n", + "Cluster: 1 - Kepadatan Tinggi (Rasio rata-rata: 28.28)\n", + " ✓ STATUS: Rasio guru sudah dalam **batas ideal**.\n", + "\n", + "Cluster: 2 - Kepadatan Sedang (Rasio rata-rata: 27.88)\n", + " ✓ STATUS: Rasio guru sudah dalam **batas ideal**.\n", + "\n", + "============================================================\n", + "EXPORT HASIL\n", + "============================================================\n", + "Hasil clustering telah disimpan ke: hasil_clustering_pendidik_sma_2024.csv\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "\n", + " async function download(id, filename, size) {\n", + " if (!google.colab.kernel.accessAllowed) {\n", + " return;\n", + " }\n", + " const div = document.createElement('div');\n", + " const label = document.createElement('label');\n", + " label.textContent = `Downloading \"${filename}\": `;\n", + " div.appendChild(label);\n", + " const progress = document.createElement('progress');\n", + " progress.max = size;\n", + " div.appendChild(progress);\n", + " document.body.appendChild(div);\n", + "\n", + " const buffers = [];\n", + " let downloaded = 0;\n", + "\n", + " const channel = await google.colab.kernel.comms.open(id);\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + "\n", + " for await (const message of channel.messages) {\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + " if (message.buffers) {\n", + " for (const buffer of message.buffers) {\n", + " buffers.push(buffer);\n", + " downloaded += buffer.byteLength;\n", + " progress.value = downloaded;\n", + " }\n", + " }\n", + " }\n", + " const blob = new Blob(buffers, {type: 'application/binary'});\n", + " const a = document.createElement('a');\n", + " a.href = window.URL.createObjectURL(blob);\n", + " a.download = filename;\n", + " div.appendChild(a);\n", + " a.click();\n", + " div.remove();\n", + " }\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "download(\"download_548c0f38-c266-4ff6-9450-1829a58e0140\", \"hasil_clustering_pendidik_sma_2024.csv\", 1243)" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "============================================================\n", + "ANALISIS SELESAI!\n", + "============================================================\n" + ] + } + ], + "source": [ + "# K-Means Clustering: Jumlah Pendidik SMA 2024\n", + "# Analisis Pengelompokan Wilayah Berdasarkan Data Pendidik\n", + "\n", + "# ==========================================\n", + "# 1. IMPORT LIBRARY\n", + "# ==========================================\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\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics import silhouette_score, davies_bouldin_score\n", + "import warnings\n", + "from google.colab import files # Tetap diperlukan untuk fungsionalitas Colab\n", + "\n", + "# Mengabaikan warning dan set style\n", + "warnings.filterwarnings('ignore')\n", + "plt.style.use('seaborn-v0_8-darkgrid')\n", + "sns.set_palette(\"husl\")\n", + "\n", + "# ==========================================\n", + "# 2. LOAD DATA\n", + "# ==========================================\n", + "# Data contoh (Hapus jika Anda menggunakan file upload)\n", + "data_contoh = {\n", + " 'wilayah': ['DKI Jakarta', 'Jawa Barat', 'Jawa Tengah', 'Jawa Timur',\n", + " 'Sumatera Utara', 'Banten', 'Sulawesi Selatan', 'Kalimantan Timur',\n", + " 'Papua', 'Maluku', 'Bali', 'NTB', 'Lampung', 'Riau',\n", + " 'Sulawesi Utara', 'Kalimantan Selatan', 'Jambi', 'Bengkulu',\n", + " 'Aceh', 'Sumatera Barat', 'NTT', 'Papua Barat', 'Gorontalo', 'Maluku Utara'],\n", + " 'jumlah_pendidik': [15000, 25000, 20000, 23000, 12000, 10000, 8000, 5000,\n", + " 3000, 2500, 6000, 4500, 7000, 6500, 4000, 5500, 4200, 3500,\n", + " 8500, 7500, 3800, 2000, 1800, 2200],\n", + " 'jumlah_sekolah': [450, 850, 750, 800, 400, 350, 300, 200,\n", + " 150, 120, 250, 180, 280, 260, 170, 220, 190, 160,\n", + " 320, 300, 190, 100, 90, 110]\n", + "}\n", + "df = pd.DataFrame(data_contoh)\n", + "\n", + "# Hitung rasio guru per sekolah\n", + "df['rasio_guru_per_sekolah'] = df['jumlah_pendidik'] / df['jumlah_sekolah']\n", + "\n", + "# ==========================================\n", + "# 3. EKSPLORASI DATA RINGKAS\n", + "# ==========================================\n", + "print(\"=\"*60)\n", + "print(\"EKSPLORASI DATA AWAL\")\n", + "print(\"=\"*60)\n", + "print(f\"\\nJumlah data: {len(df)} wilayah. Tidak ada missing value.\\n\")\n", + "print(df.head().to_markdown(index=False, numalign=\"left\", stralign=\"left\"))\n", + "print(\"\\nStatistik Deskriptif:\\n\", df.describe().round(1).to_markdown(numalign=\"left\", stralign=\"left\"))\n", + "\n", + "# ==========================================\n", + "# 4. VISUALISASI DATA AWAL (Disederhanakan)\n", + "# ==========================================\n", + "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", + "fig.suptitle('Eksplorasi Data Pendidik SMA 2024', fontsize=16, fontweight='bold')\n", + "\n", + "# Plot 1: Scatter plot\n", + "axes[0].scatter(df['jumlah_sekolah'], df['jumlah_pendidik'], alpha=0.7, s=100)\n", + "axes[0].set_xlabel('Jumlah Sekolah')\n", + "axes[0].set_ylabel('Jumlah Pendidik')\n", + "axes[0].set_title('Distribusi Pendidik vs Sekolah')\n", + "\n", + "# Plot 2: Distribusi rasio guru per sekolah\n", + "sns.histplot(df['rasio_guru_per_sekolah'], bins=15, ax=axes[1], kde=True)\n", + "axes[1].set_xlabel('Rasio Guru per Sekolah')\n", + "axes[1].set_title('Distribusi Rasio Guru per Sekolah')\n", + "\n", + "# Plot 3: Box Plot Jumlah Pendidik dan Sekolah (Menggunakan Seaborn untuk kemudahan)\n", + "sns.boxplot(data=df[['jumlah_pendidik', 'jumlah_sekolah']], ax=axes[2],\n", + " orient='v', palette=['#FFA07A', '#98D8C8'])\n", + "axes[2].set_title('Box Plot Outlier')\n", + "\n", + "plt.tight_layout(rect=[0, 0, 1, 0.95])\n", + "plt.show()\n", + "\n", + "# ==========================================\n", + "# 5. PERSIAPAN DATA UNTUK CLUSTERING\n", + "# ==========================================\n", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"PERSIAPAN DATA\")\n", + "print(\"=\"*60)\n", + "\n", + "# Pilih fitur, Normalisasi, dan Persiapan K-Means\n", + "features = ['jumlah_pendidik', 'jumlah_sekolah']\n", + "X = df[features].values\n", + "scaler = StandardScaler()\n", + "X_scaled = scaler.fit_transform(X)\n", + "\n", + "print(f\"\\nFitur yang digunakan: {features}\")\n", + "print(f\"Shape data: {X.shape}\")\n", + "print(\"Data telah dinormalisasi menggunakan StandardScaler.\")\n", + "\n", + "# ==========================================\n", + "# 6. MENENTUKAN JUMLAH CLUSTER OPTIMAL\n", + "# ==========================================\n", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"MENENTUKAN JUMLAH CLUSTER OPTIMAL\")\n", + "print(\"=\"*60)\n", + "\n", + "inertias = []\n", + "silhouette_scores = []\n", + "K_range = range(2, 11)\n", + "\n", + "for k in K_range:\n", + " kmeans = KMeans(n_clusters=k, random_state=42, n_init='auto')\n", + " kmeans.fit(X_scaled)\n", + " inertias.append(kmeans.inertia_)\n", + " silhouette_scores.append(silhouette_score(X_scaled, kmeans.labels_))\n", + "\n", + "# Visualisasi Elbow Method dan Silhouette Score\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# Elbow Method\n", + "axes[0].plot(K_range, inertias, 'bo-')\n", + "axes[0].set_xlabel('Jumlah Cluster (K)')\n", + "axes[0].set_ylabel('Inertia')\n", + "axes[0].set_title('Elbow Method')\n", + "axes[0].axvline(x=3, color='r', linestyle='--', label='K=3 (Rekomendasi)')\n", + "axes[0].legend()\n", + "\n", + "# Silhouette Score\n", + "axes[1].plot(K_range, silhouette_scores, 'go-')\n", + "axes[1].set_xlabel('Jumlah Cluster (K)')\n", + "axes[1].set_ylabel('Silhouette Score')\n", + "axes[1].set_title('Silhouette Score')\n", + "axes[1].axvline(x=3, color='r', linestyle='--', label='K=3 (Rekomendasi)')\n", + "axes[1].legend()\n", + "\n", + "plt.show()\n", + "\n", + "# Tampilkan tabel evaluasi\n", + "eval_df = pd.DataFrame({'K': list(K_range), 'Inertia': inertias, 'Silhouette Score': silhouette_scores})\n", + "print(\"\\nEvaluasi untuk berbagai K:\\n\", eval_df.to_markdown(index=False))\n", + "\n", + "# ==========================================\n", + "# 7. IMPLEMENTASI K-MEANS CLUSTERING\n", + "# ==========================================\n", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"IMPLEMENTASI K-MEANS CLUSTERING (K=3)\")\n", + "print(\"=\"*60)\n", + "\n", + "optimal_k = 3\n", + "kmeans = KMeans(n_clusters=optimal_k, random_state=42, n_init='auto')\n", + "df['cluster'] = kmeans.fit_predict(X_scaled)\n", + "\n", + "# Metrik Evaluasi Akhir\n", + "s_score = silhouette_score(X_scaled, df['cluster'])\n", + "db_score = davies_bouldin_score(X_scaled, df['cluster'])\n", + "print(f\"\\nClustering selesai! Iterasi: {kmeans.n_iter_}\")\n", + "print(f\"Inertia: {kmeans.inertia_:.2f} | Silhouette Score: {s_score:.3f} | Davies-Bouldin Index: {db_score:.3f}\")\n", + "\n", + "# ==========================================\n", + "# 8. ANALISIS HASIL CLUSTERING RINGKAS\n", + "# ==========================================\n", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"ANALISIS HASIL CLUSTERING\")\n", + "print(\"=\"*60)\n", + "\n", + "# Buat label cluster\n", + "cluster_labels = {}\n", + "# Menghitung rata-rata pendidik untuk penentuan label\n", + "avg_pendidik = df.groupby('cluster')['jumlah_pendidik'].mean()\n", + "\n", + "for i in range(optimal_k):\n", + " if avg_pendidik[i] > 15000:\n", + " cluster_labels[i] = 'Kepadatan Tinggi'\n", + " elif avg_pendidik[i] > 7000:\n", + " cluster_labels[i] = 'Kepadatan Sedang'\n", + " else:\n", + " cluster_labels[i] = 'Kepadatan Rendah'\n", + "\n", + "df['label_cluster'] = df['cluster'].map(cluster_labels)\n", + "\n", + "# Ringkasan per cluster\n", + "summary = df.groupby('cluster').agg(\n", + " wilayah=('wilayah', 'count'),\n", + " pendidik_avg=('jumlah_pendidik', 'mean'),\n", + " sekolah_avg=('jumlah_sekolah', 'mean'),\n", + " rasio_avg=('rasio_guru_per_sekolah', 'mean')\n", + ").round(2).rename(index=cluster_labels)\n", + "\n", + "print(\"\\nRingkasan Statistik per Cluster:\\n\", summary.to_markdown(numalign=\"left\", stralign=\"left\"))\n", + "\n", + "# Tampilkan wilayah per cluster\n", + "print(\"\\nWilayah dalam setiap Cluster:\")\n", + "for i in range(optimal_k):\n", + " wilayah_list = df[df['cluster'] == i]['wilayah'].values\n", + " print(f\" • Cluster {i} ({cluster_labels[i]}): {', '.join(wilayah_list)}\")\n", + "\n", + "\n", + "# ==========================================\n", + "# 9. VISUALISASI HASIL CLUSTERING (Disederhanakan)\n", + "# ==========================================\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n", + "fig.suptitle('Hasil K-Means Clustering: Pendidik SMA 2024', fontsize=16, fontweight='bold')\n", + "\n", + "# Plot 1: Scatter plot dengan cluster\n", + "centroids = scaler.inverse_transform(kmeans.cluster_centers_)\n", + "\n", + "sns.scatterplot(data=df, x='jumlah_sekolah', y='jumlah_pendidik', hue='label_cluster',\n", + " style='label_cluster', s=150, ax=axes[0], palette='viridis', legend='full',\n", + " edgecolor='black')\n", + "axes[0].scatter(centroids[:, 1], centroids[:, 0], marker='X', s=400, c='red',\n", + " edgecolors='black', linewidth=2, label='Centroid', zorder=5)\n", + "axes[0].set_xlabel('Jumlah Sekolah')\n", + "axes[0].set_ylabel('Jumlah Pendidik')\n", + "axes[0].set_title('Scatter Plot: Hasil Clustering (Pendidik vs Sekolah)')\n", + "axes[0].legend(title='Cluster')\n", + "\n", + "\n", + "# Plot 2: Box plot rasio guru per sekolah per cluster\n", + "sns.boxplot(data=df, x='label_cluster', y='rasio_guru_per_sekolah', ax=axes[1],\n", + " palette='viridis')\n", + "axes[1].set_xlabel('Cluster')\n", + "axes[1].set_ylabel('Rasio Guru per Sekolah')\n", + "axes[1].set_title('Perbandingan Rasio Guru per Sekolah per Cluster')\n", + "\n", + "plt.tight_layout(rect=[0, 0, 1, 0.95])\n", + "plt.show()\n", + "\n", + "# ==========================================\n", + "# 10. INTERPRETASI DAN REKOMENDASI RINGKAS\n", + "# ==========================================\n", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"INTERPRETASI & REKOMENDASI\")\n", + "print(\"=\"*60)\n", + "\n", + "for cluster, label in cluster_labels.items():\n", + " avg_rasio = summary.loc[label, 'rasio_avg']\n", + " print(f\"\\nCluster: {cluster} - {label} (Rasio rata-rata: {avg_rasio:.2f})\")\n", + "\n", + " if avg_rasio < 25:\n", + " print(\" REKOMENDASI: **PENAMBAHAN guru** (Rasio di bawah standar ideal 25-30)\")\n", + " elif avg_rasio > 35:\n", + " print(\" REKOMENDASI: **SURPLUS guru** (Pertimbangkan redistribusi)\")\n", + " else:\n", + " print(\" ✓ STATUS: Rasio guru sudah dalam **batas ideal**.\")\n", + "\n", + "# ==========================================\n", + "# 11. EXPORT HASIL\n", + "# ==========================================\n", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"EXPORT HASIL\")\n", + "print(\"=\"*60)\n", + "\n", + "hasil_df = df[['wilayah', 'jumlah_pendidik', 'jumlah_sekolah',\n", + " 'rasio_guru_per_sekolah', 'cluster', 'label_cluster']].sort_values('cluster')\n", + "hasil_df.to_csv('hasil_clustering_pendidik_sma_2024.csv', index=False)\n", + "print(\"Hasil clustering telah disimpan ke: hasil_clustering_pendidik_sma_2024.csv\")\n", + "\n", + "try:\n", + " files.download('hasil_clustering_pendidik_sma_2024.csv')\n", + "except Exception as e:\n", + " print(\"Gagal auto-download. Pastikan Anda menjalankan kode di Google Colab.\")\n", + "\n", + "print(\"\\n\" + \"=\"*60)\n", + "print(\"ANALISIS SELESAI!\")\n", + "print(\"=\"*60)" + ] + } + ] +} \ No newline at end of file diff --git a/CLUSTERING/PERBANDINGAN_K_MEANS_VS_HIERARCHICAL_CLUSTERING.ipynb b/CLUSTERING/PERBANDINGAN_K_MEANS_VS_HIERARCHICAL_CLUSTERING.ipynb new file mode 100644 index 0000000..8da91b9 --- /dev/null +++ b/CLUSTERING/PERBANDINGAN_K_MEANS_VS_HIERARCHICAL_CLUSTERING.ipynb @@ -0,0 +1,379 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "84Y-t6-85f5j", + "outputId": "87f36f05-2ab7-407d-bab4-4b0b3b8ef6b3" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "==================================================\n", + "PERBANDINGAN K-MEANS VS HIERARCHICAL\n", + "==================================================\n", + "Silhouette Score K-Means : 0.595\n", + "Silhouette Score Hierarchical : 0.599\n", + "\n", + "Jumlah Wilayah per Cluster:\n", + "K-Means:\n", + " cluster_kmeans\n", + "0 14\n", + "1 3\n", + "2 7\n", + "Name: count, dtype: int64\n", + "\n", + "Hierarchical:\n", + " cluster_hierarchical\n", + "0 9\n", + "1 3\n", + "2 12\n", + "Name: count, dtype: int64\n", + "\n", + "Tabel Perbandingan Metode Clustering:\n", + "\n", + "| Metode | Jumlah Cluster | Silhouette Score |\n", + "|:-------------|-----------------:|-------------------:|\n", + "| K-Means | 3 | 0.595 |\n", + "| Hierarchical | 3 | 0.599 |\n", + "============================================================\n", + "EVALUASI HIERARCHICAL CLUSTERING\n", + "============================================================\n", + "Silhouette Score : 0.599\n", + "Davies-Bouldin Index : 0.460\n", + "\n", + "Ringkasan Statistik per Cluster:\n", + "\n", + "| cluster | wilayah | pendidik_avg | sekolah_avg | rasio_avg |\n", + "|:-----------------|:----------|:---------------|:--------------|:------------|\n", + "| Kepadatan Sedang | 9 | 8944.44 | 323.33 | 27.13 |\n", + "| Kepadatan Tinggi | 3 | 22666.7 | 800 | 28.28 |\n", + "| Kepadatan Rendah | 12 | 3500 | 156.67 | 21.95 |\n", + "\n", + "Wilayah dalam setiap Cluster:\n", + " • Cluster 0 (Kepadatan Sedang): DKI Jakarta, Sumatera Utara, Banten, Sulawesi Selatan, Bali, Lampung, Riau, Aceh, Sumatera Barat\n", + " • Cluster 1 (Kepadatan Tinggi): Jawa Barat, Jawa Tengah, Jawa Timur\n", + " • Cluster 2 (Kepadatan Rendah): Kalimantan Timur, Papua, Maluku, NTB, Sulawesi Utara, Kalimantan Selatan, Jambi, Bengkulu, NTT, Papua Barat, Gorontalo, Maluku Utara\n" + ] + }, + { + "output_type": "display_data", + 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Hasil clustering disimpan ke: hasil_hierarchical_pendidik_sma_2024.csv\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "\n", + " async function download(id, filename, size) {\n", + " if (!google.colab.kernel.accessAllowed) {\n", + " return;\n", + " }\n", + " const div = document.createElement('div');\n", + " const label = document.createElement('label');\n", + " label.textContent = `Downloading \"${filename}\": `;\n", + " div.appendChild(label);\n", + " const progress = document.createElement('progress');\n", + " progress.max = size;\n", + " div.appendChild(progress);\n", + " document.body.appendChild(div);\n", + "\n", + " const buffers = [];\n", + " let downloaded = 0;\n", + "\n", + " const channel = await google.colab.kernel.comms.open(id);\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + "\n", + " for await (const message of channel.messages) {\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + " if (message.buffers) {\n", + " for (const buffer of message.buffers) {\n", + " buffers.push(buffer);\n", + " downloaded += buffer.byteLength;\n", + " progress.value = downloaded;\n", + " }\n", + " }\n", + " }\n", + " const blob = new Blob(buffers, {type: 'application/binary'});\n", + " const a = document.createElement('a');\n", + " a.href = window.URL.createObjectURL(blob);\n", + " a.download = filename;\n", + " div.appendChild(a);\n", + " a.click();\n", + " div.remove();\n", + " }\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "download(\"download_18f52ccd-9cf7-408c-9f81-adb5059db09d\", \"hasil_hierarchical_pendidik_sma_2024.csv\", 1243)" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "============================================================\n", + "ANALISIS HIERARCHICAL CLUSTERING SELESAI\n", + "============================================================\n" + ] + } + ], + "source": [ + "# Hierarchical Clustering: Jumlah Pendidik SMA 2024\n", + "# Analisis Pengelompokan Wilayah Berdasarkan Data Pendidik\n", + "\n", + "# ==========================================\n", + "# 1. IMPORT LIBRARY\n", + "# ==========================================\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.cluster import AgglomerativeClustering\n", + "from sklearn.metrics import silhouette_score, davies_bouldin_score\n", + "from scipy.cluster.hierarchy import dendrogram, linkage\n", + "import warnings\n", + "from google.colab import files\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "plt.style.use('seaborn-v0_8-darkgrid')\n", + "sns.set_palette(\"husl\")\n", + "\n", + "# ==========================================\n", + "# 2. LOAD DATA\n", + "# ==========================================\n", + "data_contoh = {\n", + " 'wilayah': ['DKI Jakarta', 'Jawa Barat', 'Jawa Tengah', 'Jawa Timur',\n", + " 'Sumatera Utara', 'Banten', 'Sulawesi Selatan', 'Kalimantan Timur',\n", + " 'Papua', 'Maluku', 'Bali', 'NTB', 'Lampung', 'Riau',\n", + " 'Sulawesi Utara', 'Kalimantan Selatan', 'Jambi', 'Bengkulu',\n", + " 'Aceh', 'Sumatera Barat', 'NTT', 'Papua Barat', 'Gorontalo', 'Maluku Utara'],\n", + " 'jumlah_pendidik': [15000, 25000, 20000, 23000, 12000, 10000, 8000, 5000,\n", + " 3000, 2500, 6000, 4500, 7000, 6500, 4000, 5500, 4200, 3500,\n", + " 8500, 7500, 3800, 2000, 1800, 2200],\n", + " 'jumlah_sekolah': [450, 850, 750, 800, 400, 350, 300, 200,\n", + " 150, 120, 250, 180, 280, 260, 170, 220, 190, 160,\n", + " 320, 300, 190, 100, 90, 110]\n", + "}\n", + "\n", + "df = pd.DataFrame(data_contoh)\n", + "df['rasio_guru_per_sekolah'] = df['jumlah_pendidik'] / df['jumlah_sekolah']\n", + "\n", + "# ==========================================\n", + "# 3. PERSIAPAN DATA\n", + "# ==========================================\n", + "features = ['jumlah_pendidik', 'jumlah_sekolah']\n", + "X = df[features].values\n", + "scaler = StandardScaler()\n", + "X_scaled = scaler.fit_transform(X)\n", + "\n", + "# ==========================================\n", + "# 4. DENDROGRAM (MENENTUKAN JUMLAH CLUSTER)\n", + "# ==========================================\n", + "plt.figure(figsize=(12, 6))\n", + "linked = linkage(X_scaled, method='ward')\n", + "dendrogram(linked,\n", + " labels=df['wilayah'].values,\n", + " orientation='top',\n", + " distance_sort='descending',\n", + " show_leaf_counts=True)\n", + "plt.title('Dendrogram Hierarchical Clustering')\n", + "plt.xlabel('Wilayah')\n", + "plt.ylabel('Jarak')\n", + "plt.xticks(rotation=90)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# ==========================================\n", + "# 5. PERBANDINGAN DENGAN K-MEANS\n", + "# ==========================================\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.metrics import silhouette_score\n", + "\n", + "# K-Means Clustering\n", + "kmeans = KMeans(n_clusters=3, random_state=42, n_init='auto')\n", + "df['cluster_kmeans'] = kmeans.fit_predict(X_scaled)\n", + "sil_kmeans = silhouette_score(X_scaled, df['cluster_kmeans'])\n", + "\n", + "# Hierarchical Clustering\n", + "hc = AgglomerativeClustering(n_clusters=3, linkage='ward')\n", + "df['cluster_hierarchical'] = hc.fit_predict(X_scaled)\n", + "sil_hc = silhouette_score(X_scaled, df['cluster_hierarchical'])\n", + "\n", + "# ==========================================\n", + "# 6. HASIL PERBANDINGAN\n", + "# ==========================================\n", + "print(\"=\"*50)\n", + "print(\"PERBANDINGAN K-MEANS VS HIERARCHICAL\")\n", + "print(\"=\"*50)\n", + "print(f\"Silhouette Score K-Means : {sil_kmeans:.3f}\")\n", + "print(f\"Silhouette Score Hierarchical : {sil_hc:.3f}\")\n", + "\n", + "# Ringkasan jumlah wilayah per cluster\n", + "print(\"\\nJumlah Wilayah per Cluster:\")\n", + "print(\"K-Means:\\n\", df['cluster_kmeans'].value_counts().sort_index())\n", + "print(\"\\nHierarchical:\\n\", df['cluster_hierarchical'].value_counts().sort_index())\n", + "\n", + "# ==========================================\n", + "# 7. TABEL PERBANDINGAN SEDERHANA\n", + "# ==========================================\n", + "perbandingan = pd.DataFrame({\n", + " 'Metode': ['K-Means', 'Hierarchical'],\n", + " 'Jumlah Cluster': [3, 3],\n", + " 'Silhouette Score': [sil_kmeans, sil_hc]\n", + "})\n", + "\n", + "print(\"\\nTabel Perbandingan Metode Clustering:\\n\")\n", + "print(perbandingan.round(3).to_markdown(index=False))\n", + "plt.show()\n", + "\n", + "# ==========================================\n", + "# 5. IMPLEMENTASI HIERARCHICAL CLUSTERING\n", + "# ==========================================\n", + "optimal_cluster = 3\n", + "hc = AgglomerativeClustering(n_clusters=optimal_cluster, metric='euclidean', linkage='ward')\n", + "df['cluster'] = hc.fit_predict(X_scaled)\n", + "\n", + "# ==========================================\n", + "# 6. EVALUASI CLUSTERING\n", + "# ==========================================\n", + "s_score = silhouette_score(X_scaled, df['cluster'])\n", + "db_score = davies_bouldin_score(X_scaled, df['cluster'])\n", + "\n", + "print(\"=\"*60)\n", + "print(\"EVALUASI HIERARCHICAL CLUSTERING\")\n", + "print(\"=\"*60)\n", + "print(f\"Silhouette Score : {s_score:.3f}\")\n", + "print(f\"Davies-Bouldin Index : {db_score:.3f}\")\n", + "\n", + "# ==========================================\n", + "# 7. ANALISIS HASIL CLUSTERING\n", + "# ==========================================\n", + "cluster_labels = {}\n", + "avg_pendidik = df.groupby('cluster')['jumlah_pendidik'].mean()\n", + "\n", + "for i in range(optimal_cluster):\n", + " if avg_pendidik[i] > 15000:\n", + " cluster_labels[i] = 'Kepadatan Tinggi'\n", + " elif avg_pendidik[i] > 7000:\n", + " cluster_labels[i] = 'Kepadatan Sedang'\n", + " else:\n", + " cluster_labels[i] = 'Kepadatan Rendah'\n", + "\n", + "df['label_cluster'] = df['cluster'].map(cluster_labels)\n", + "\n", + "summary = df.groupby('cluster').agg(\n", + " wilayah=('wilayah', 'count'),\n", + " pendidik_avg=('jumlah_pendidik', 'mean'),\n", + " sekolah_avg=('jumlah_sekolah', 'mean'),\n", + " rasio_avg=('rasio_guru_per_sekolah', 'mean')\n", + ").round(2).rename(index=cluster_labels)\n", + "\n", + "print(\"\\nRingkasan Statistik per Cluster:\\n\")\n", + "print(summary.to_markdown(numalign=\"left\", stralign=\"left\"))\n", + "\n", + "print(\"\\nWilayah dalam setiap Cluster:\")\n", + "for i in range(optimal_cluster):\n", + " wilayah_list = df[df['cluster'] == i]['wilayah'].values\n", + " print(f\" • Cluster {i} ({cluster_labels[i]}): {', '.join(wilayah_list)}\")\n", + "\n", + "# ==========================================\n", + "# 8. VISUALISASI HASIL CLUSTERING\n", + "# ==========================================\n", + "plt.figure(figsize=(8, 6))\n", + "sns.scatterplot(data=df,\n", + " x='jumlah_sekolah',\n", + " y='jumlah_pendidik',\n", + " hue='label_cluster',\n", + " style='label_cluster',\n", + " s=150,\n", + " palette='viridis',\n", + " edgecolor='black')\n", + "plt.title('Hierarchical Clustering: Pendidik vs Sekolah')\n", + "plt.xlabel('Jumlah Sekolah')\n", + "plt.ylabel('Jumlah Pendidik')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# ==========================================\n", + "# 9. EXPORT HASIL\n", + "# ==========================================\n", + "hasil_df = df[['wilayah', 'jumlah_pendidik', 'jumlah_sekolah',\n", + " 'rasio_guru_per_sekolah', 'cluster', 'label_cluster']].sort_values('cluster')\n", + "\n", + "hasil_df.to_csv('hasil_hierarchical_pendidik_sma_2024.csv', index=False)\n", + "print(\"Hasil clustering disimpan ke: hasil_hierarchical_pendidik_sma_2024.csv\")\n", + "\n", + "try:\n", + " files.download('hasil_hierarchical_pendidik_sma_2024.csv')\n", + "except:\n", + " print(\"Auto-download hanya berfungsi di Google Colab\")\n", + "\n", + "print(\"=\"*60)\n", + "print(\"ANALISIS HIERARCHICAL CLUSTERING SELESAI\")\n", + "print(\"=\"*60)\n" + ] + } + ] +} \ No newline at end of file