{ "cells": [ { "cell_type": "markdown", "id": "9d179189", "metadata": {}, "source": [ "# Pelatihan Model Klasifikasi — SISTA MIF" ] }, { "cell_type": "markdown", "id": "f932c049", "metadata": {}, "source": [ "## 1. Import Library & Konfigurasi" ] }, { "cell_type": "code", "execution_count": 1, "id": "cf9c85ad", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Konfigurasi selesai.\n", " Training file : d:\\Project\\sista_mif_ta\\data\\processed\\training_corpus.csv\n", " Test file : d:\\Project\\sista_mif_ta\\data\\processed\\test_set.csv\n", " Model output : d:\\Project\\sista_mif_ta\\fastapi\\ml_assets\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import re\n", "import joblib\n", "import json\n", "import warnings\n", "from pathlib import Path\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.model_selection import StratifiedKFold\n", "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n", "from sklearn.pipeline import Pipeline\n", "import matplotlib.pyplot as plt\n", "import matplotlib.ticker as mticker\n", "\n", "%matplotlib inline\n", "\n", "BASE_DIR = Path.cwd()\n", "TRAIN_FILE = BASE_DIR / \"processed\" / \"training_corpus.csv\"\n", "TEST_FILE = BASE_DIR / \"processed\" / \"test_set.csv\"\n", "ML_DIR = BASE_DIR.parent / \"fastapi\" / \"ml_assets\"\n", "ML_DIR.mkdir(parents=True, exist_ok=True)\n", "\n", "TARGET_CLASSES = [\"Programmer\", \"Data Analyst\", \"Wirausaha Informatika\", \"Non-IT\"]\n", "CONFIDENCE_THRESHOLD = 0.50\n", "COLORS = [\"#4C72B0\", \"#DD8452\", \"#55A868\", \"#C44E52\"]\n", "\n", "STOPWORDS = {\n", " \"yang\", \"di\", \"ke\", \"dari\", \"dan\", \"atau\", \"dengan\", \"untuk\", \"pada\", \"dalam\",\n", " \"adalah\", \"ini\", \"itu\", \"tidak\", \"juga\", \"sudah\", \"akan\", \"bisa\", \"ada\", \"oleh\",\n", " \"karena\", \"secara\", \"serta\", \"sebagai\", \"bagi\", \"telah\", \"maka\", \"namun\", \"sehingga\",\n", " \"jika\", \"agar\", \"ketika\", \"saat\", \"sebelum\", \"sesudah\", \"hingga\", \"sampai\", \"antara\",\n", " \"sekitar\", \"hanya\", \"saja\", \"belum\", \"masih\", \"lagi\", \"pun\", \"justru\", \"walaupun\",\n", " \"meskipun\", \"bahkan\", \"cukup\", \"sangat\", \"paling\", \"lebih\", \"kurang\", \"lain\",\n", " \"macam\", \"cara\", \"hal\", \"tentang\", \"mengenai\", \"terhadap\", \"kepada\", \"menuju\",\n", " \"kecuali\", \"selain\", \"tanpa\", \"demi\", \"guna\", \"khususnya\", \"umumnya\", \"kebanyakan\",\n", " \"sebagian\", \"beberapa\", \"semua\", \"setiap\", \"tiap\", \"satu\", \"dua\", \"tiga\", \"empat\",\n", " \"lima\", \"enam\", \"tujuh\", \"delapan\", \"sembilan\", \"sepuluh\", \"ratus\", \"ribu\", \"juta\"\n", "}\n", "\n", "print(\"Konfigurasi selesai.\")\n", "print(f\" Training file : {TRAIN_FILE}\")\n", "print(f\" Test file : {TEST_FILE}\")\n", "print(f\" Model output : {ML_DIR}\")" ] }, { "cell_type": "markdown", "id": "665600e3", "metadata": {}, "source": [ "## 2. Definisi Fungsi & Pipeline" ] }, { "cell_type": "code", "execution_count": 2, "id": "87e60ec2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pipeline TF-IDF + Logistic Regression siap.\n", " - TF-IDF : max_features=3000, ngram=(1,2), sublinear_tf=True, min_df=2\n", " - LR : max_iter=2000, class_weight='balanced', solver='lbfgs'\n" ] } ], "source": [ "def preprocess_text(text):\n", " \"\"\"Pembersihan dasar — tanpa stemming ulang karena korpus sudah di-stem.\"\"\"\n", " if pd.isna(text) or not isinstance(text, str): return \"\"\n", " text = text.lower().strip()\n", " if text in {\"nan\", \"none\", \"null\", \"-\", \"0\", \"\", \"tidak diisi\"}: return \"\"\n", " text = text.replace('-', ' ').replace('/', ' ').replace('_', ' ').replace(',', ' ')\n", " text = re.sub(r'[^\\w\\s]', '', text)\n", " text = re.sub(r'\\d+', '', text)\n", " words = [w for w in text.split() if w not in STOPWORDS and len(w) > 2]\n", " return \" \".join(words)\n", "\n", "def get_pipeline():\n", " \"\"\"Fungsi helper untuk memastikan konsistensi pipeline antara K-Fold dan Final Model.\"\"\"\n", " return Pipeline([\n", " ('tfidf', TfidfVectorizer(\n", " max_features=3000,\n", " ngram_range=(1, 2),\n", " sublinear_tf=True,\n", " min_df=2,\n", " norm='l2'\n", " )),\n", " ('clf', LogisticRegression(\n", " max_iter=2000,\n", " class_weight='balanced',\n", " solver='lbfgs',\n", " random_state=42\n", " ))\n", " ])\n", "\n", "print(\"Pipeline TF-IDF + Logistic Regression siap.\")\n", "print(\" - TF-IDF : max_features=3000, ngram=(1,2), sublinear_tf=True, min_df=2\")\n", "print(\" - LR : max_iter=2000, class_weight='balanced', solver='lbfgs'\")" ] }, { "cell_type": "markdown", "id": "0888b6bd", "metadata": {}, "source": [ "## 3. Memuat & Memproses Data" ] }, { "cell_type": "code", "execution_count": 3, "id": "285eb81b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sedang memproses teks...\n", "\n", "Data siap:\n", " Jumlah data latih : 250 baris\n", " Jumlah data uji : 107 baris\n", "\n", "Distribusi label training:\n", "label\n", "Wirausaha Informatika 89\n", "Non-IT 87\n", "Programmer 68\n", "Data Analyst 6\n" ] } ], "source": [ "assert TRAIN_FILE.exists(), \"File training_corpus.csv tidak ditemukan. Jalankan prepare_corpus.ipynb terlebih dahulu.\"\n", "\n", "df_train = pd.read_csv(TRAIN_FILE, sep=\";\", dtype=str).dropna(subset=[\"job_text_raw\", \"label\"])\n", "df_test = pd.read_csv(TEST_FILE, sep=\";\", dtype=str).dropna(subset=[\"job_text_raw\", \"label\"]) if TEST_FILE.exists() else None\n", "\n", "print(\"Sedang memproses teks...\")\n", "X_train = df_train[\"job_text_raw\"].apply(preprocess_text)\n", "y_train = df_train[\"label\"]\n", "mask = X_train.str.len() > 0\n", "X_train, y_train = X_train[mask], y_train[mask]\n", "\n", "X_test, y_test = pd.Series(dtype=str), pd.Series(dtype=str)\n", "if df_test is not None:\n", " X_test = df_test[\"job_text_raw\"].apply(preprocess_text)\n", " y_test = df_test[\"label\"]\n", " mask_t = X_test.str.len() > 0\n", " X_test, y_test = X_test[mask_t], y_test[mask_t]\n", "\n", "print(f\"\\nData siap:\")\n", "print(f\" Jumlah data latih : {len(X_train)} baris\")\n", "print(f\" Jumlah data uji : {len(X_test)} baris\")\n", "print(\"\\nDistribusi label training:\")\n", "print(y_train.value_counts().to_string())" ] }, { "cell_type": "markdown", "id": "2cb77033", "metadata": {}, "source": [ "## 4. K-Fold Cross Validation (5 Lipatan)" ] }, { "cell_type": "code", "execution_count": 4, "id": "9cdd6602", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Melakukan pengujian K-Fold (5 putaran) pada data latih...\n", " Fold 1/5 -> Akurasi: 0.8200\n", " Fold 2/5 -> Akurasi: 0.7400\n", " Fold 3/5 -> Akurasi: 0.7200\n", " Fold 4/5 -> Akurasi: 0.7200\n", " Fold 5/5 -> Akurasi: 0.5600\n", "\n", "RATA-RATA PENGUJIAN K-FOLD:\n", " Akurasi Keseluruhan : 0.7120 +/- 0.0845\n", "\n", " Programmer | P: 0.978 | R: 0.398 | F1: 0.549\n", " Data Analyst | P: 0.132 | R: 0.500 | F1: 0.207\n", " Wirausaha Informatika | P: 0.788 | R: 0.737 | F1: 0.746\n", " Non-IT | P: 0.706 | R: 0.942 | F1: 0.805\n" ] } ], "source": [ "print(\"Melakukan pengujian K-Fold (5 putaran) pada data latih...\")\n", "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n", "fold_metrics = []\n", "\n", "for i, (tr_idx, te_idx) in enumerate(skf.split(X_train, y_train)):\n", " model = get_pipeline()\n", " model.fit(X_train.iloc[tr_idx], y_train.iloc[tr_idx])\n", " y_pred = model.predict(X_train.iloc[te_idx])\n", " rep = classification_report(y_train.iloc[te_idx], y_pred, output_dict=True, zero_division=0)\n", " fold_metrics.append(rep)\n", " print(f\" Fold {i+1}/5 -> Akurasi: {rep['accuracy']:.4f}\")\n", "\n", "avg_acc = np.mean([f['accuracy'] for f in fold_metrics])\n", "std_acc = np.std([f['accuracy'] for f in fold_metrics])\n", "\n", "print(f\"\\nRATA-RATA PENGUJIAN K-FOLD:\")\n", "print(f\" Akurasi Keseluruhan : {avg_acc:.4f} +/- {std_acc:.4f}\")\n", "print()\n", "for cls in TARGET_CLASSES:\n", " p = np.mean([f.get(cls, {}).get('precision', 0) for f in fold_metrics])\n", " r = np.mean([f.get(cls, {}).get('recall', 0) for f in fold_metrics])\n", " f1 = np.mean([f.get(cls, {}).get('f1-score', 0) for f in fold_metrics])\n", " print(f\" {cls:25} | P: {p:.3f} | R: {r:.3f} | F1: {f1:.3f}\")" ] }, { "cell_type": "markdown", "id": "c1828f40", "metadata": {}, "source": [ "### Visualisasi Akurasi per Fold" ] }, { "cell_type": "code", "execution_count": 5, "id": "11067a1b", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fold_accs = [f['accuracy'] for f in fold_metrics]\n", "\n", "fig, ax = plt.subplots(figsize=(8, 4))\n", "bars = ax.bar([f\"Fold {i+1}\" for i in range(5)], fold_accs,\n", " color=COLORS[0], edgecolor=\"white\", linewidth=0.8, alpha=0.9)\n", "ax.axhline(avg_acc, color=\"red\", linestyle=\"--\", linewidth=1.5, label=f\"Rata-rata: {avg_acc:.4f}\")\n", "ax.fill_between(range(5), avg_acc - std_acc, avg_acc + std_acc,\n", " alpha=0.15, color=\"red\", label=f\"± Std Dev: {std_acc:.4f}\")\n", "for bar, val in zip(bars, fold_accs):\n", " ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.002,\n", " f\"{val:.4f}\", ha=\"center\", va=\"bottom\", fontsize=9)\n", "ax.set_ylim(min(fold_accs) * 0.95, 1.0)\n", "ax.set_xlabel(\"Fold\"); ax.set_ylabel(\"Akurasi\")\n", "ax.set_title(\"Akurasi K-Fold Cross Validation (5 Lipatan)\", fontsize=12, fontweight=\"bold\")\n", "ax.legend(); ax.spines[[\"top\", \"right\"]].set_visible(False)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "913f7707", "metadata": {}, "source": [ "### Visualisasi F1-Score per Kelas (Rata-rata K-Fold)" ] }, { "cell_type": "code", "execution_count": 6, "id": "59984ccb", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "metrics_summary = {\n", " cls: {\n", " \"precision\": np.mean([f.get(cls, {}).get('precision', 0) for f in fold_metrics]),\n", " \"recall\": np.mean([f.get(cls, {}).get('recall', 0) for f in fold_metrics]),\n", " \"f1\": np.mean([f.get(cls, {}).get('f1-score', 0) for f in fold_metrics]),\n", " }\n", " for cls in TARGET_CLASSES\n", "}\n", "\n", "x = np.arange(len(TARGET_CLASSES))\n", "width = 0.25\n", "\n", "fig, ax = plt.subplots(figsize=(11, 5))\n", "for j, (metric, color) in enumerate([(\"precision\", \"#4C72B0\"), (\"recall\", \"#DD8452\"), (\"f1\", \"#55A868\")]):\n", " vals = [metrics_summary[cls][metric] for cls in TARGET_CLASSES]\n", " bars = ax.bar(x + j * width, vals, width, label=metric.capitalize(), color=color, edgecolor=\"white\", alpha=0.9)\n", " for bar, val in zip(bars, vals):\n", " ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.005,\n", " f\"{val:.2f}\", ha=\"center\", va=\"bottom\", fontsize=8)\n", "\n", "ax.set_xticks(x + width)\n", "ax.set_xticklabels(TARGET_CLASSES, rotation=10)\n", "ax.set_ylim(0, 1.1); ax.set_ylabel(\"Skor\")\n", "ax.set_title(\"Precision / Recall / F1-Score per Kelas (Rata-rata K-Fold)\", fontsize=12, fontweight=\"bold\")\n", "ax.legend(); ax.spines[[\"top\", \"right\"]].set_visible(False)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "a737c575", "metadata": {}, "source": [ "## 5. Pelatihan Model Final" ] }, { "cell_type": "code", "execution_count": 7, "id": "63615b5f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Membuat model final dari seluruh data latih yang tersedia...\n", "Model final berhasil dilatih.\n" ] } ], "source": [ "print(\"Membuat model final dari seluruh data latih yang tersedia...\")\n", "final_model = get_pipeline()\n", "final_model.fit(X_train, y_train)\n", "print(\"Model final berhasil dilatih.\")" ] }, { "cell_type": "markdown", "id": "04c06078", "metadata": {}, "source": [ "## 6. Evaluasi pada Hold-Out Test Set" ] }, { "cell_type": "code", "execution_count": 8, "id": "2b517946", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HASIL PENGUJIAN PADA DATA TEST:\n", " precision recall f1-score support\n", "\n", " Data Analyst 0.00 0.00 0.00 2\n", " Non-IT 0.77 0.94 0.85 36\n", " Programmer 0.95 0.63 0.76 30\n", "Wirausaha Informatika 0.86 0.92 0.89 39\n", "\n", " accuracy 0.83 107\n", " macro avg 0.64 0.63 0.62 107\n", " weighted avg 0.84 0.83 0.82 107\n", "\n", "Catatan: Kelas 'Data Analyst' hanya punya 2.0 data uji — skor F1-nya mungkin kurang akurat.\n" ] } ], "source": [ "metrics_test = None\n", "if len(X_test) > 0:\n", " y_pred = final_model.predict(X_test)\n", " rep_test = classification_report(y_test, y_pred, output_dict=True, zero_division=0)\n", " metrics_test = rep_test\n", "\n", " print(\"HASIL PENGUJIAN PADA DATA TEST:\")\n", " print(classification_report(y_test, y_pred, zero_division=0))\n", "\n", " for cls in TARGET_CLASSES:\n", " sup = rep_test[cls]['support'] if cls in rep_test else 0\n", " if sup < 5:\n", " print(f\"Catatan: Kelas '{cls}' hanya punya {sup} data uji — skor F1-nya mungkin kurang akurat.\")\n", "else:\n", " print(\"Tidak ada data test. Lewati evaluasi.\")" ] }, { "cell_type": "markdown", "id": "fb152996", "metadata": {}, "source": [ "### 6a. Confusion Matrix" ] }, { "cell_type": "code", "execution_count": 9, "id": "756c106b", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " Disimpan ke: d:\\Project\\sista_mif_ta\\fastapi\\ml_assets\\confusion_matrix_test.png\n" ] } ], "source": [ "if len(X_test) > 0:\n", " cm = confusion_matrix(y_test, y_pred, labels=TARGET_CLASSES)\n", " disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=TARGET_CLASSES)\n", "\n", " fig, ax = plt.subplots(figsize=(7, 6))\n", " disp.plot(cmap='Blues', values_format='d', xticks_rotation=30, colorbar=True, ax=ax)\n", " ax.set_title('Confusion Matrix — Hold-Out Test Set', fontsize=12, fontweight='bold', pad=15)\n", " plt.tight_layout()\n", " plt.savefig(ML_DIR / 'confusion_matrix_test.png', dpi=300, bbox_inches='tight')\n", " plt.show()\n", " print(f\" Disimpan ke: {ML_DIR / 'confusion_matrix_test.png'}\")" ] }, { "cell_type": "markdown", "id": "c3c98bcc", "metadata": {}, "source": [ "### 6b. Distribusi Confidence Score" ] }, { "cell_type": "code", "execution_count": 10, "id": "41588b9e", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " 46 dari 107 prediksi (43.0%) di bawah threshold 0.5.\n", " Disimpan ke: d:\\Project\\sista_mif_ta\\fastapi\\ml_assets\\confidence_distribution.png\n" ] } ], "source": [ "if len(X_test) > 0:\n", " proba = final_model.predict_proba(X_test)\n", " max_p = np.max(proba, axis=1)\n", " below = np.sum(max_p < CONFIDENCE_THRESHOLD)\n", "\n", " fig, ax = plt.subplots(figsize=(9, 4))\n", " ax.hist(max_p, bins=15, edgecolor='black', alpha=0.75, color='teal', label='Distribusi confidence')\n", " ax.axvline(x=CONFIDENCE_THRESHOLD, color='red', linestyle='--', linewidth=2,\n", " label=f'Threshold ({CONFIDENCE_THRESHOLD})')\n", " ax.axvspan(0, CONFIDENCE_THRESHOLD, alpha=0.07, color='red', label=f'Di bawah threshold: {below} ({below/len(max_p)*100:.1f}%)')\n", " ax.set_xlabel('Confidence Score', fontsize=11)\n", " ax.set_ylabel('Jumlah Prediksi', fontsize=11)\n", " ax.set_title('Distribusi Confidence Score Model', fontsize=12, fontweight='bold')\n", " ax.legend(); ax.grid(axis='y', alpha=0.3)\n", " ax.spines[[\"top\", \"right\"]].set_visible(False)\n", " plt.tight_layout()\n", " plt.savefig(ML_DIR / 'confidence_distribution.png', dpi=300, bbox_inches='tight')\n", " plt.show()\n", "\n", " print(f\"\\n {below} dari {len(max_p)} prediksi ({below/len(max_p)*100:.1f}%) di bawah threshold {CONFIDENCE_THRESHOLD}.\")\n", " print(f\" Disimpan ke: {ML_DIR / 'confidence_distribution.png'}\")" ] }, { "cell_type": "markdown", "id": "03ebc2a4", "metadata": {}, "source": [ "### 6c. Feature Importance (Kata Paling Berpengaruh)" ] }, { "cell_type": "code", "execution_count": 11, "id": "5fab1fc4", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "3 KATA PALING BERPENGARUH (POSITIF & NEGATIF) TIAP KELAS:\n", " [Data Analyst]\n", " Mendorong (+) : analyst(2.84), admin(1.81), system analyst(1.36)\n", " Menghambat (-): staff(-1.11), founder(-0.83), programmer(-0.25)\n", " [Non-IT]\n", " Mendorong (+) : staff(0.81), staf(0.61), teller(0.54)\n", " Menghambat (-): founder(-1.61), analyst(-1.17), freelance(-1.01)\n", " [Programmer]\n", " Mendorong (+) : teknisi(1.31), staff teknisi(1.31), programmer(1.24)\n", " Menghambat (-): analyst(-1.02), admin(-0.88), system analyst(-0.51)\n", " [Wirausaha Informatika]\n", " Mendorong (+) : founder(2.14), kerja lepas(0.61), kerja(0.61)\n", " Menghambat (-): analyst(-0.66), admin(-0.52), system(-0.34)\n" ] } ], "source": [ "if len(X_test) > 0:\n", " vec = final_model.named_steps['tfidf']\n", " clf = final_model.named_steps['clf']\n", " feats = vec.get_feature_names_out()\n", " coeffs = clf.coef_\n", "\n", " TOP_N = 10\n", " fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n", " fig.suptitle(f\"Top {TOP_N} Kata Berpengaruh per Kelas (Koefisien Logistic Regression)\",\n", " fontsize=13, fontweight=\"bold\")\n", "\n", " for ax, (cls_idx, cls) in zip(axes.flat, enumerate(clf.classes_)):\n", " coef = coeffs[cls_idx]\n", " top_pos_idx = coef.argsort()[-TOP_N:][::-1]\n", " top_neg_idx = coef.argsort()[:TOP_N]\n", " combined_idx = np.concatenate([top_pos_idx, top_neg_idx])\n", " combined_vals = coef[combined_idx]\n", " combined_words = feats[combined_idx]\n", " colors_bar = [\"#4C72B0\" if v > 0 else \"#C44E52\" for v in combined_vals]\n", "\n", " sorted_order = np.argsort(combined_vals)\n", " ax.barh(combined_words[sorted_order], combined_vals[sorted_order],\n", " color=[colors_bar[i] for i in sorted_order], edgecolor='white')\n", " ax.axvline(0, color='black', linewidth=0.8)\n", " ax.set_title(f\"[{cls}]\", fontsize=11, fontweight=\"bold\")\n", " ax.set_xlabel(\"Koefisien\")\n", " ax.spines[[\"top\", \"right\"]].set_visible(False)\n", "\n", " plt.tight_layout()\n", " plt.show()\n", "\n", " print(\"\\n3 KATA PALING BERPENGARUH (POSITIF & NEGATIF) TIAP KELAS:\")\n", " for i, cls in enumerate(clf.classes_):\n", " pos_idx = coeffs[i].argsort()[-3:][::-1]\n", " neg_idx = coeffs[i].argsort()[:3]\n", " top_pos = [(feats[j], coeffs[i][j]) for j in pos_idx if coeffs[i][j] > 0]\n", " top_neg = [(feats[j], coeffs[i][j]) for j in neg_idx if coeffs[i][j] < 0]\n", " pos_str = \", \".join([f\"{w}({c:.2f})\" for w, c in top_pos]) if top_pos else \"Tidak ada\"\n", " neg_str = \", \".join([f\"{w}({c:.2f})\" for w, c in top_neg]) if top_neg else \"Tidak ada\"\n", " print(f\" [{cls}]\")\n", " print(f\" Mendorong (+) : {pos_str}\")\n", " print(f\" Menghambat (-): {neg_str}\")" ] }, { "cell_type": "markdown", "id": "a90948c7", "metadata": {}, "source": [ "### 6d. Prediksi yang Meleset" ] }, { "cell_type": "code", "execution_count": 12, "id": "62ff108f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "18 prediksi meleset dari 107 total data uji:\n", "\n" ] } ], "source": [ "if len(X_test) > 0:\n", " mis = np.where(y_test.values != y_pred)[0]\n", " if len(mis) > 0:\n", " print(f\"{len(mis)} prediksi meleset dari {len(y_test)} total data uji:\\n\")\n", " rows = []\n", " for idx in mis[:min(10, len(mis))]:\n", " rows.append({\n", " \"Teks (cuplikan)\": df_test.iloc[idx]['job_text_raw'][:60] + \"...\",\n", " \"Label Seharusnya\": y_test.iloc[idx],\n", " \"Prediksi Model\": y_pred[idx],\n", " })\n", " pd.DataFrame(rows)\n", " else:\n", " print(\"Semua prediksi pada test set benar!\")" ] }, { "cell_type": "markdown", "id": "14917bc5", "metadata": {}, "source": [ "## 7. Simpan Model & Metrik" ] }, { "cell_type": "code", "execution_count": 13, "id": "2f972209", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model berhasil disimpan di : d:\\Project\\sista_mif_ta\\fastapi\\ml_assets\\ml_pipeline_internal.pkl\n", "Metrik disimpan di : d:\\Project\\sista_mif_ta\\fastapi\\ml_assets\\metrics_internal_only.json\n", "\n", " Akurasi rata-rata K-Fold : 0.7120 +/- 0.0845\n", " Weighted F1 (test set) : 0.8231\n" ] } ], "source": [ "model_path = ML_DIR / \"ml_pipeline_internal.pkl\"\n", "joblib.dump(final_model, model_path)\n", "\n", "metrics = {\n", " \"methodology\": \"Internal MIF only\",\n", " \"k_fold\": {\n", " \"accuracy_mean\": float(avg_acc),\n", " \"accuracy_std\": float(np.std([f['accuracy'] for f in fold_metrics])),\n", " \"folds\": fold_metrics\n", " },\n", " \"threshold_config\": CONFIDENCE_THRESHOLD\n", "}\n", "if metrics_test:\n", " metrics[\"hold_out_test\"] = metrics_test\n", "\n", "metrics_file = ML_DIR / \"metrics_internal_only.json\"\n", "with open(metrics_file, 'w', encoding='utf-8') as f:\n", " json.dump(metrics, f, indent=2, ensure_ascii=False)\n", "\n", "print(f\"Model berhasil disimpan di : {model_path}\")\n", "print(f\"Metrik disimpan di : {metrics_file}\")\n", "print(f\"\\n Akurasi rata-rata K-Fold : {avg_acc:.4f} +/- {np.std([f['accuracy'] for f in fold_metrics]):.4f}\")\n", "if metrics_test:\n", " print(f\" Weighted F1 (test set) : {metrics_test.get('weighted avg', {}).get('f1-score', 0):.4f}\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }