From 548ce6675cde5fa80a0a6f5ed8e4734d13a2a1d9 Mon Sep 17 00:00:00 2001 From: 202310715001 MUHAMMAD BINTANG MUDZAFFAR <202310715001@mhs.ubharajaya.ac.id> Date: Sun, 23 Nov 2025 23:29:39 +0700 Subject: [PATCH] Delete tugas_kelompok_PROYEKML.ipynb --- tugas_kelompok_PROYEKML.ipynb | 796 ---------------------------------- 1 file changed, 796 deletions(-) delete mode 100644 tugas_kelompok_PROYEKML.ipynb diff --git a/tugas_kelompok_PROYEKML.ipynb b/tugas_kelompok_PROYEKML.ipynb deleted file mode 100644 index ede79f2..0000000 --- a/tugas_kelompok_PROYEKML.ipynb +++ /dev/null @@ -1,796 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "7FFKv8RmyMPM", - "outputId": "0c0ab66d-6891-40c4-ecae-fd6c829047b0" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Upload file CSV Anda (atau skip untuk menggunakan data contoh)\n", - "Format kolom: wilayah, jumlah_pendidik, jumlah_sekolah\n", - "\n", - "============================================================\n", - "EKSPLORASI DATA AWAL\n", - "============================================================\n", - "\n", - " 5 Data Teratas:\n", - " wilayah jumlah_pendidik jumlah_sekolah\n", - "0 DKI Jakarta 15000 450\n", - "1 Jawa Barat 25000 850\n", - "2 Jawa Tengah 20000 750\n", - "3 Jawa Timur 23000 800\n", - "4 Sumatera Utara 12000 400\n", - "\n", - " Statistik Deskriptif:\n", - " jumlah_pendidik jumlah_sekolah\n", - "count 24.000000 24.000000\n", - "mean 7937.500000 299.583333\n", - "std 6573.385899 214.830149\n", - "min 1800.000000 90.000000\n", - "25% 3725.000000 167.500000\n", - "50% 5750.000000 235.000000\n", - "75% 8875.000000 327.500000\n", - "max 25000.000000 850.000000\n", - "\n", - " Info Dataset:\n", - "\n", - "RangeIndex: 24 entries, 0 to 23\n", - "Data columns (total 3 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 wilayah 24 non-null object\n", - " 1 jumlah_pendidik 24 non-null int64 \n", - " 2 jumlah_sekolah 24 non-null int64 \n", - "dtypes: int64(2), object(1)\n", - "memory usage: 708.0+ bytes\n", - "None\n", - "\n", - " Jumlah data: 24 wilayah\n", - " Tidak ada missing value: True\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", - "\n", - " Data sebelum normalisasi (5 data pertama):\n", - "[[15000 450]\n", - " [25000 850]\n", - " [20000 750]\n", - " [23000 800]\n", - " [12000 400]]\n", - "\n", - " Data setelah normalisasi (5 data pertama):\n", - "[[1.09751644 0.71522458]\n", - " [2.65152202 2.61720686]\n", - " [1.87451923 2.14171129]\n", - " [2.34072091 2.37945908]\n", - " [0.63131477 0.4774768 ]]\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", - " 2 10.467361 0.731571\n", - " 3 3.662624 0.596419\n", - " 4 1.808707 0.573437\n", - " 5 1.173294 0.557479\n", - " 6 0.830915 0.501176\n", - " 7 0.532124 0.472502\n", - " 8 0.357063 0.457985\n", - " 9 0.280502 0.430821\n", - "10 0.203941 0.396541\n", - "\n", - "============================================================\n", - "IMPLEMENTASI K-MEANS CLUSTERING\n", - "============================================================\n", - "\n", - " Jumlah cluster yang dipilih: K = 3\n", - "\n", - " Clustering selesai!\n", - " Jumlah iterasi: 2\n", - " Inertia: 3.66\n", - " Silhouette Score: 0.596\n", - " Davies-Bouldin Index: 0.466\n", - "\n", - "============================================================\n", - "ANALISIS HASIL CLUSTERING\n", - "============================================================\n", - "\n", - "============================================================\n", - " CLUSTER 0\n", - "============================================================\n", - "Jumlah wilayah: 13\n", - "Rata-rata jumlah pendidik: 3692\n", - "Rata-rata jumlah sekolah: 164\n", - "Rata-rata rasio guru/sekolah: 22.10\n", - "\n", - "Wilayah dalam cluster:\n", - " • Kalimantan Timur\n", - " • Papua\n", - " • Maluku\n", - " • Bali\n", - " • NTB\n", - " • Sulawesi Utara\n", - " • Kalimantan Selatan\n", - " • Jambi\n", - " • Bengkulu\n", - " • NTT\n", - " • Papua Barat\n", - " • Gorontalo\n", - " • Maluku Utara\n", - "\n", - "============================================================\n", - " CLUSTER 1\n", - "============================================================\n", - "Jumlah wilayah: 3\n", - "Rata-rata jumlah pendidik: 22667\n", - "Rata-rata jumlah sekolah: 800\n", - "Rata-rata rasio guru/sekolah: 28.28\n", - "\n", - "Wilayah dalam cluster:\n", - " • Jawa Barat\n", - " • Jawa Tengah\n", - " • Jawa Timur\n", - "\n", - "============================================================\n", - " CLUSTER 2\n", - "============================================================\n", - "Jumlah wilayah: 8\n", - "Rata-rata jumlah pendidik: 9312\n", - "Rata-rata jumlah sekolah: 332\n", - "Rata-rata rasio guru/sekolah: 27.52\n", - "\n", - "Wilayah dalam cluster:\n", - " • DKI Jakarta\n", - " • Sumatera Utara\n", - " • Banten\n", - " • Sulawesi Selatan\n", - " • Lampung\n", - " • Riau\n", - " • Aceh\n", - " • Sumatera Barat\n", - "\n", - "============================================================\n", - "VISUALISASI HASIL CLUSTERING\n", - "============================================================\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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- }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "============================================================\n", - "TABEL HASIL CLUSTERING\n", - "============================================================\n", - "\n", - " Hasil Lengkap Clustering:\n", - " wilayah jumlah_pendidik jumlah_sekolah rasio_guru_per_sekolah cluster label_cluster\n", - " Kalimantan Timur 5000 200 25.000000 0 Kepadatan Rendah\n", - " Bali 6000 250 24.000000 0 Kepadatan Rendah\n", - " Sulawesi Utara 4000 170 23.529412 0 Kepadatan Rendah\n", - "Kalimantan Selatan 5500 220 25.000000 0 Kepadatan Rendah\n", - " NTB 4500 180 25.000000 0 Kepadatan Rendah\n", - " Papua 3000 150 20.000000 0 Kepadatan Rendah\n", - " Maluku 2500 120 20.833333 0 Kepadatan Rendah\n", - " Jambi 4200 190 22.105263 0 Kepadatan Rendah\n", - " NTT 3800 190 20.000000 0 Kepadatan Rendah\n", - " Papua Barat 2000 100 20.000000 0 Kepadatan Rendah\n", - " Maluku Utara 2200 110 20.000000 0 Kepadatan Rendah\n", - " Gorontalo 1800 90 20.000000 0 Kepadatan Rendah\n", - " Bengkulu 3500 160 21.875000 0 Kepadatan Rendah\n", - " Jawa Timur 23000 800 28.750000 1 Kepadatan Tinggi\n", - " Jawa Tengah 20000 750 26.666667 1 Kepadatan Tinggi\n", - " Jawa Barat 25000 850 29.411765 1 Kepadatan Tinggi\n", - " Lampung 7000 280 25.000000 2 Kepadatan Sedang\n", - " Riau 6500 260 25.000000 2 Kepadatan Sedang\n", - " Banten 10000 350 28.571429 2 Kepadatan Sedang\n", - " Sulawesi Selatan 8000 300 26.666667 2 Kepadatan Sedang\n", - " DKI Jakarta 15000 450 33.333333 2 Kepadatan Sedang\n", - " Sumatera Utara 12000 400 30.000000 2 Kepadatan Sedang\n", - " Sumatera Barat 7500 300 25.000000 2 Kepadatan Sedang\n", - " Aceh 8500 320 26.562500 2 Kepadatan Sedang\n", - "\n", - "============================================================\n", - "RINGKASAN STATISTIK PER CLUSTER\n", - "============================================================\n", - " wilayah jumlah_pendidik jumlah_sekolah \\\n", - " count mean min max mean min max \n", - "cluster \n", - "0 13 3692.31 1800 6000 163.85 90 250 \n", - "1 3 22666.67 20000 25000 800.00 750 850 \n", - "2 8 9312.50 6500 15000 332.50 260 450 \n", - "\n", - " rasio_guru_per_sekolah \n", - " mean min max \n", - "cluster \n", - "0 22.10 20.00 25.00 \n", - "1 28.28 26.67 29.41 \n", - "2 27.52 25.00 33.33 \n", - "\n", - "============================================================\n", - "INTERPRETASI & REKOMENDASI\n", - "============================================================\n", - "\n", - " Cluster 0 - Kepadatan Rendah:\n", - " • Jumlah wilayah: 13\n", - " • Rasio rata-rata: 22.10 guru per sekolah\n", - " REKOMENDASI: Wilayah ini memerlukan PENAMBAHAN guru\n", - " Rasio di bawah standar nasional (25-30 guru/sekolah)\n", - "\n", - " Cluster 1 - Kepadatan Tinggi:\n", - " • Jumlah wilayah: 3\n", - " • Rasio rata-rata: 28.28 guru per sekolah\n", - " ✓ STATUS: Rasio guru sudah dalam batas ideal\n", - "\n", - " Cluster 2 - Kepadatan Sedang:\n", - " • Jumlah wilayah: 8\n", - " • Rasio rata-rata: 27.52 guru per sekolah\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_d9535bdd-825c-49c5-9a47-541735bb0840\", \"hasil_clustering_pendidik_sma_2024.csv\", 1243)" - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "File berhasil didownload!\n", - "\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", - "warnings.filterwarnings('ignore')\n", - "\n", - "# Set style\n", - "plt.style.use('seaborn-v0_8-darkgrid')\n", - "sns.set_palette(\"husl\")\n", - "\n", - "# ==========================================\n", - "# 2. LOAD DATA\n", - "# ==========================================\n", - "# Contoh 1: Upload file dari komputer lokal\n", - "from google.colab import files\n", - "print(\"Upload file CSV Anda (atau skip untuk menggunakan data contoh)\")\n", - "print(\"Format kolom: wilayah, jumlah_pendidik, jumlah_sekolah\")\n", - "\n", - "# Uncomment baris di bawah untuk upload file\n", - "# uploaded = files.upload()\n", - "# df = pd.read_csv(list(uploaded.keys())[0])\n", - "\n", - "# Data contoh (hapus jika sudah upload file sendiri)\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", - "\n", - "# ==========================================\n", - "# 3. EKSPLORASI DATA\n", - "# ==========================================\n", - "print(\"\\n\" + \"=\"*60)\n", - "print(\"EKSPLORASI DATA AWAL\")\n", - "print(\"=\"*60)\n", - "print(\"\\n 5 Data Teratas:\")\n", - "print(df.head())\n", - "\n", - "print(\"\\n Statistik Deskriptif:\")\n", - "print(df.describe())\n", - "\n", - "print(\"\\n Info Dataset:\")\n", - "print(df.info())\n", - "\n", - "print(f\"\\n Jumlah data: {len(df)} wilayah\")\n", - "print(f\" Tidak ada missing value: {df.isnull().sum().sum() == 0}\")\n", - "\n", - "# Hitung rasio guru per sekolah\n", - "df['rasio_guru_per_sekolah'] = df['jumlah_pendidik'] / df['jumlah_sekolah']\n", - "\n", - "# ==========================================\n", - "# 4. VISUALISASI DATA AWAL\n", - "# ==========================================\n", - "fig, axes = plt.subplots(2, 2, figsize=(15, 12))\n", - "fig.suptitle('Eksplorasi Data Pendidik SMA 2024', fontsize=16, fontweight='bold')\n", - "\n", - "# Plot 1: Scatter plot\n", - "axes[0, 0].scatter(df['jumlah_sekolah'], df['jumlah_pendidik'],\n", - " alpha=0.6, s=100, c='steelblue', edgecolors='black')\n", - "axes[0, 0].set_xlabel('Jumlah Sekolah', fontsize=12)\n", - "axes[0, 0].set_ylabel('Jumlah Pendidik', fontsize=12)\n", - "axes[0, 0].set_title('Distribusi Pendidik vs Sekolah', fontsize=14)\n", - "axes[0, 0].grid(True, alpha=0.3)\n", - "\n", - "# Plot 2: Top 10 wilayah berdasarkan jumlah pendidik\n", - "top_10 = df.nlargest(10, 'jumlah_pendidik')\n", - "axes[0, 1].barh(top_10['wilayah'], top_10['jumlah_pendidik'], color='coral')\n", - "axes[0, 1].set_xlabel('Jumlah Pendidik', fontsize=12)\n", - "axes[0, 1].set_title('Top 10 Wilayah: Jumlah Pendidik', fontsize=14)\n", - "axes[0, 1].grid(True, alpha=0.3, axis='x')\n", - "\n", - "# Plot 3: Distribusi rasio guru per sekolah\n", - "axes[1, 0].hist(df['rasio_guru_per_sekolah'], bins=15, color='lightgreen',\n", - " edgecolor='black', alpha=0.7)\n", - "axes[1, 0].set_xlabel('Rasio Guru per Sekolah', fontsize=12)\n", - "axes[1, 0].set_ylabel('Frekuensi', fontsize=12)\n", - "axes[1, 0].set_title('Distribusi Rasio Guru per Sekolah', fontsize=14)\n", - "axes[1, 0].grid(True, alpha=0.3)\n", - "\n", - "# Plot 4: Box plot\n", - "box_data = [df['jumlah_pendidik'], df['jumlah_sekolah']]\n", - "axes[1, 1].boxplot(box_data, labels=['Jumlah Pendidik', 'Jumlah Sekolah'])\n", - "axes[1, 1].set_ylabel('Nilai', fontsize=12)\n", - "axes[1, 1].set_title('Box Plot: Deteksi Outlier', fontsize=14)\n", - "axes[1, 1].grid(True, alpha=0.3)\n", - "\n", - "plt.tight_layout()\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 untuk clustering\n", - "features = ['jumlah_pendidik', 'jumlah_sekolah']\n", - "X = df[features].values\n", - "\n", - "print(f\"\\n Fitur yang digunakan: {features}\")\n", - "print(f\" Shape data: {X.shape}\")\n", - "\n", - "# Normalisasi data menggunakan StandardScaler\n", - "scaler = StandardScaler()\n", - "X_scaled = scaler.fit_transform(X)\n", - "\n", - "print(\"\\n Data sebelum normalisasi (5 data pertama):\")\n", - "print(X[:5])\n", - "print(\"\\n Data setelah normalisasi (5 data pertama):\")\n", - "print(X_scaled[:5])\n", - "\n", - "# ==========================================\n", - "# 6. MENENTUKAN JUMLAH CLUSTER OPTIMAL (ELBOW METHOD)\n", - "# ==========================================\n", - "print(\"\\n\" + \"=\"*60)\n", - "print(\"MENENTUKAN JUMLAH CLUSTER OPTIMAL\")\n", - "print(\"=\"*60)\n", - "\n", - "# Hitung inertia untuk berbagai nilai K\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=10)\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=(15, 5))\n", - "\n", - "# Elbow Method\n", - "axes[0].plot(K_range, inertias, 'bo-', linewidth=2, markersize=8)\n", - "axes[0].set_xlabel('Jumlah Cluster (K)', fontsize=12)\n", - "axes[0].set_ylabel('Inertia (Within-Cluster Sum of Squares)', fontsize=12)\n", - "axes[0].set_title('Elbow Method untuk Menentukan K Optimal', fontsize=14, fontweight='bold')\n", - "axes[0].grid(True, alpha=0.3)\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-', linewidth=2, markersize=8)\n", - "axes[1].set_xlabel('Jumlah Cluster (K)', fontsize=12)\n", - "axes[1].set_ylabel('Silhouette Score', fontsize=12)\n", - "axes[1].set_title('Silhouette Score untuk Berbagai K', fontsize=14, fontweight='bold')\n", - "axes[1].grid(True, alpha=0.3)\n", - "axes[1].axvline(x=3, color='r', linestyle='--', label='K=3 (Rekomendasi)')\n", - "axes[1].legend()\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# Tampilkan tabel evaluasi\n", - "eval_df = pd.DataFrame({\n", - " 'K': list(K_range),\n", - " 'Inertia': inertias,\n", - " 'Silhouette Score': silhouette_scores\n", - "})\n", - "print(\"\\n Evaluasi untuk berbagai K:\")\n", - "print(eval_df.to_string(index=False))\n", - "\n", - "# ==========================================\n", - "# 7. IMPLEMENTASI K-MEANS CLUSTERING\n", - "# ==========================================\n", - "print(\"\\n\" + \"=\"*60)\n", - "print(\"IMPLEMENTASI K-MEANS CLUSTERING\")\n", - "print(\"=\"*60)\n", - "\n", - "# Pilih jumlah cluster optimal (bisa diubah sesuai analisis)\n", - "optimal_k = 3\n", - "print(f\"\\n Jumlah cluster yang dipilih: K = {optimal_k}\")\n", - "\n", - "# Terapkan K-Means\n", - "kmeans = KMeans(n_clusters=optimal_k, random_state=42, n_init=10)\n", - "df['cluster'] = kmeans.fit_predict(X_scaled)\n", - "\n", - "# Informasi clustering\n", - "print(f\"\\n Clustering selesai!\")\n", - "print(f\" Jumlah iterasi: {kmeans.n_iter_}\")\n", - "print(f\" Inertia: {kmeans.inertia_:.2f}\")\n", - "print(f\" Silhouette Score: {silhouette_score(X_scaled, df['cluster']):.3f}\")\n", - "print(f\" Davies-Bouldin Index: {davies_bouldin_score(X_scaled, df['cluster']):.3f}\")\n", - "\n", - "# ==========================================\n", - "# 8. ANALISIS HASIL CLUSTERING\n", - "# ==========================================\n", - "print(\"\\n\" + \"=\"*60)\n", - "print(\"ANALISIS HASIL CLUSTERING\")\n", - "print(\"=\"*60)\n", - "\n", - "# Statistik per cluster\n", - "for i in range(optimal_k):\n", - " cluster_data = df[df['cluster'] == i]\n", - " print(f\"\\n{'='*60}\")\n", - " print(f\" CLUSTER {i}\")\n", - " print(f\"{'='*60}\")\n", - " print(f\"Jumlah wilayah: {len(cluster_data)}\")\n", - " print(f\"Rata-rata jumlah pendidik: {cluster_data['jumlah_pendidik'].mean():.0f}\")\n", - " print(f\"Rata-rata jumlah sekolah: {cluster_data['jumlah_sekolah'].mean():.0f}\")\n", - " print(f\"Rata-rata rasio guru/sekolah: {cluster_data['rasio_guru_per_sekolah'].mean():.2f}\")\n", - " print(f\"\\nWilayah dalam cluster:\")\n", - " for wilayah in cluster_data['wilayah'].values:\n", - " print(f\" • {wilayah}\")\n", - "\n", - "# Buat label cluster yang lebih deskriptif\n", - "cluster_labels = {}\n", - "for i in range(optimal_k):\n", - " avg_pendidik = df[df['cluster'] == i]['jumlah_pendidik'].mean()\n", - " if avg_pendidik > 15000:\n", - " cluster_labels[i] = 'Kepadatan Tinggi'\n", - " elif avg_pendidik > 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", - "# ==========================================\n", - "# 9. VISUALISASI HASIL CLUSTERING\n", - "# ==========================================\n", - "print(\"\\n\" + \"=\"*60)\n", - "print(\"VISUALISASI HASIL CLUSTERING\")\n", - "print(\"=\"*60)\n", - "\n", - "fig, axes = plt.subplots(2, 2, figsize=(16, 14))\n", - "fig.suptitle('Hasil K-Means Clustering: Pendidik SMA 2024',\n", - " fontsize=16, fontweight='bold')\n", - "\n", - "# Plot 1: Scatter plot dengan cluster\n", - "colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#FFA07A', '#98D8C8']\n", - "for i in range(optimal_k):\n", - " cluster_data = df[df['cluster'] == i]\n", - " axes[0, 0].scatter(cluster_data['jumlah_sekolah'],\n", - " cluster_data['jumlah_pendidik'],\n", - " label=f'Cluster {i}: {cluster_labels[i]}',\n", - " alpha=0.6, s=150, c=colors[i], edgecolors='black', linewidth=1.5)\n", - "\n", - "# Plot centroid\n", - "centroids = scaler.inverse_transform(kmeans.cluster_centers_)\n", - "axes[0, 0].scatter(centroids[:, 1], centroids[:, 0],\n", - " marker='X', s=400, c='red', edgecolors='black',\n", - " linewidth=2, label='Centroid', zorder=5)\n", - "\n", - "axes[0, 0].set_xlabel('Jumlah Sekolah', fontsize=12, fontweight='bold')\n", - "axes[0, 0].set_ylabel('Jumlah Pendidik', fontsize=12, fontweight='bold')\n", - "axes[0, 0].set_title('Scatter Plot: Hasil Clustering', fontsize=14, fontweight='bold')\n", - "axes[0, 0].legend(loc='best', fontsize=10)\n", - "axes[0, 0].grid(True, alpha=0.3)\n", - "\n", - "# Plot 2: Distribusi wilayah per cluster\n", - "cluster_counts = df['cluster'].value_counts().sort_index()\n", - "bars = axes[0, 1].bar(range(optimal_k), cluster_counts.values,\n", - " color=colors[:optimal_k], edgecolor='black', linewidth=1.5)\n", - "axes[0, 1].set_xlabel('Cluster', fontsize=12, fontweight='bold')\n", - "axes[0, 1].set_ylabel('Jumlah Wilayah', fontsize=12, fontweight='bold')\n", - "axes[0, 1].set_title('Distribusi Wilayah per Cluster', fontsize=14, fontweight='bold')\n", - "axes[0, 1].set_xticks(range(optimal_k))\n", - "axes[0, 1].set_xticklabels([f'Cluster {i}\\n{cluster_labels[i]}'\n", - " for i in range(optimal_k)])\n", - "axes[0, 1].grid(True, alpha=0.3, axis='y')\n", - "\n", - "# Tambahkan nilai di atas bar\n", - "for i, bar in enumerate(bars):\n", - " height = bar.get_height()\n", - " axes[0, 1].text(bar.get_x() + bar.get_width()/2., height,\n", - " f'{int(height)}',\n", - " ha='center', va='bottom', fontweight='bold')\n", - "\n", - "# Plot 3: Box plot rasio guru per sekolah per cluster\n", - "cluster_data_list = [df[df['cluster'] == i]['rasio_guru_per_sekolah'].values\n", - " for i in range(optimal_k)]\n", - "bp = axes[1, 0].boxplot(cluster_data_list, patch_artist=True,\n", - " labels=[f'C{i}' for i in range(optimal_k)])\n", - "\n", - "for patch, color in zip(bp['boxes'], colors[:optimal_k]):\n", - " patch.set_facecolor(color)\n", - " patch.set_alpha(0.6)\n", - "\n", - "axes[1, 0].set_xlabel('Cluster', fontsize=12, fontweight='bold')\n", - "axes[1, 0].set_ylabel('Rasio Guru per Sekolah', fontsize=12, fontweight='bold')\n", - "axes[1, 0].set_title('Perbandingan Rasio Guru per Sekolah', fontsize=14, fontweight='bold')\n", - "axes[1, 0].grid(True, alpha=0.3, axis='y')\n", - "\n", - "# Plot 4: Heatmap statistik cluster\n", - "stats_data = []\n", - "for i in range(optimal_k):\n", - " cluster_df = df[df['cluster'] == i]\n", - " stats_data.append([\n", - " cluster_df['jumlah_pendidik'].mean(),\n", - " cluster_df['jumlah_sekolah'].mean(),\n", - " cluster_df['rasio_guru_per_sekolah'].mean()\n", - " ])\n", - "\n", - "stats_matrix = np.array(stats_data).T\n", - "im = axes[1, 1].imshow(stats_matrix, cmap='YlOrRd', aspect='auto')\n", - "axes[1, 1].set_xticks(range(optimal_k))\n", - "axes[1, 1].set_xticklabels([f'Cluster {i}' for i in range(optimal_k)])\n", - "axes[1, 1].set_yticks(range(3))\n", - "axes[1, 1].set_yticklabels(['Rata-rata\\nPendidik', 'Rata-rata\\nSekolah',\n", - " 'Rata-rata\\nRasio'])\n", - "axes[1, 1].set_title('Heatmap Statistik per Cluster', fontsize=14, fontweight='bold')\n", - "\n", - "# Tambahkan nilai di dalam heatmap\n", - "for i in range(3):\n", - " for j in range(optimal_k):\n", - " text = axes[1, 1].text(j, i, f'{stats_matrix[i, j]:.1f}',\n", - " ha=\"center\", va=\"center\", color=\"black\",\n", - " fontweight='bold', fontsize=10)\n", - "\n", - "plt.colorbar(im, ax=axes[1, 1])\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# ==========================================\n", - "# 10. TABEL HASIL CLUSTERING\n", - "# ==========================================\n", - "print(\"\\n\" + \"=\"*60)\n", - "print(\"TABEL HASIL CLUSTERING\")\n", - "print(\"=\"*60)\n", - "\n", - "# Urutkan berdasarkan cluster\n", - "hasil_df = df[['wilayah', 'jumlah_pendidik', 'jumlah_sekolah',\n", - " 'rasio_guru_per_sekolah', 'cluster', 'label_cluster']].sort_values('cluster')\n", - "\n", - "print(\"\\n Hasil Lengkap Clustering:\")\n", - "print(hasil_df.to_string(index=False))\n", - "\n", - "# Ringkasan per cluster\n", - "print(\"\\n\" + \"=\"*60)\n", - "print(\"RINGKASAN STATISTIK PER CLUSTER\")\n", - "print(\"=\"*60)\n", - "\n", - "summary = df.groupby('cluster').agg({\n", - " 'wilayah': 'count',\n", - " 'jumlah_pendidik': ['mean', 'min', 'max'],\n", - " 'jumlah_sekolah': ['mean', 'min', 'max'],\n", - " 'rasio_guru_per_sekolah': ['mean', 'min', 'max']\n", - "}).round(2)\n", - "\n", - "print(summary)\n", - "\n", - "# ==========================================\n", - "# 11. INTERPRETASI DAN REKOMENDASI\n", - "# ==========================================\n", - "print(\"\\n\" + \"=\"*60)\n", - "print(\"INTERPRETASI & REKOMENDASI\")\n", - "print(\"=\"*60)\n", - "\n", - "for i in range(optimal_k):\n", - " cluster_df = df[df['cluster'] == i]\n", - " avg_rasio = cluster_df['rasio_guru_per_sekolah'].mean()\n", - "\n", - " print(f\"\\n Cluster {i} - {cluster_labels[i]}:\")\n", - " print(f\" • Jumlah wilayah: {len(cluster_df)}\")\n", - " print(f\" • Rasio rata-rata: {avg_rasio:.2f} guru per sekolah\")\n", - "\n", - " if avg_rasio < 25:\n", - " print(f\" REKOMENDASI: Wilayah ini memerlukan PENAMBAHAN guru\")\n", - " print(f\" Rasio di bawah standar nasional (25-30 guru/sekolah)\")\n", - " elif avg_rasio > 35:\n", - " print(f\" REKOMENDASI: Wilayah ini SURPLUS guru\")\n", - " print(f\" Pertimbangkan redistribusi ke wilayah kekurangan\")\n", - " else:\n", - " print(f\" ✓ STATUS: Rasio guru sudah dalam batas ideal\")\n", - "\n", - "# ==========================================\n", - "# 12. EXPORT HASIL\n", - "# ==========================================\n", - "print(\"\\n\" + \"=\"*60)\n", - "print(\"EXPORT HASIL\")\n", - "print(\"=\"*60)\n", - "\n", - "# Simpan hasil ke CSV\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", - "# Download file (khusus Google Colab)\n", - "try:\n", - " files.download('hasil_clustering_pendidik_sma_2024.csv')\n", - " print(\"File berhasil didownload!\")\n", - "except:\n", - " print(\"Jalankan di Google Colab untuk auto-download\")\n", - "\n", - "print(\"\\n\" + \"=\"*60)\n", - "print(\"ANALISIS SELESAI!\")\n", - "print(\"=\"*60)" - ] - } - ] -} \ No newline at end of file