From e479b552e0f87f79988295ae558c18972b3d654d Mon Sep 17 00:00:00 2001 From: Kavic Date: Mon, 10 Nov 2025 23:18:24 +0700 Subject: [PATCH 1/5] feat: TUGAS KELOMPOK --- feature_names.pkl | Bin 0 -> 115 bytes hasil_perbandingan.xlsx | Bin 0 -> 5695 bytes heart.csv | 304 ++++++++++++++++++++++++++++++++++++++++ kelompok.pkl | Bin 0 -> 618089 bytes kelompok.py | 218 ++++++++++++++++++++++++++++ roc_curve.png | Bin 0 -> 50670 bytes 6 files changed, 522 insertions(+) create mode 100644 feature_names.pkl create mode 100644 hasil_perbandingan.xlsx create mode 100644 heart.csv create mode 100644 kelompok.pkl create mode 100644 kelompok.py create mode 100644 roc_curve.png diff --git a/feature_names.pkl b/feature_names.pkl new file mode 100644 index 0000000000000000000000000000000000000000..3ec2be1cde5b0b2e8791c823c822cc7c5b4c8a49 GIT binary patch literal 115 zcmXAhu?~PB3`C7+^i!PtBZpEeBoZOQz~~2btp96?H{HE!?-^d-{n}(<1CE4ErYhWH 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'Confusion_Manual' di hasil_perbandingan.xlsx\n" + ] + } + ], + "source": [ + "# ===============================================\n", + "# TUGAS : Machine Learning - Perbandingan 3 Algoritma\n", + "# DATA : Heart Disease Dataset (heart.csv)\n", + "# ===============================================\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import joblib\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.linear_model import LogisticRegression\n", + "from openpyxl import Workbook\n", + "\n", + "# ===============================================\n", + "# 1. LOAD DATASET\n", + "# ===============================================\n", + "df = pd.read_csv(\"heart.csv\")\n", + "print(\"Kolom dataset:\", df.columns)\n", + "\n", + "# Pisahkan fitur (X) dan target (y)\n", + "X = df.drop(\"target\", axis=1)\n", + "y = df[\"target\"]\n", + "\n", + "# Bagi data menjadi 70% training dan 30% testing\n", + "x_train, x_temp, y_temp, y_test = train_test_split(\n", + " X, y, test_size=0.3, random_state=42, stratify=y\n", + "\n", + ")\n", + "\n", + "# ===============================================\n", + "# 2. INISIALISASI 3 MODEL DAN LATIH\n", + "# ===============================================\n", + "models = {\n", + " \"Decision Tree\": DecisionTreeClassifier(random_state=42),\n", + " \"Random Forest\": RandomForestClassifier(random_state=42),\n", + " \"Logistic Regression\": LogisticRegression(max_iter=1000, random_state=42)\n", + "}\n", + "\n", + "hasil = []\n", + "roc_data = {}\n", + "\n", + "for nama, model in models.items():\n", + " model.fit(x_train, y_temp)\n", + " y_pred = model.predict(x_temp)\n", + " y_prob = model.predict_proba(x_temp)[:, 1]\n", + "\n", + " cm = confusion_matrix(y_test, y_pred)\n", + " tn, fp, fn, tp = cm.ravel()\n", + " \n", + " acc = accuracy_score(y_test, y_pred)\n", + " auc = roc_auc_score(y_test, y_prob)\n", + "\n", + " print(f\"\\n=== {nama} ===\")\n", + " print(\"Confusion Matrix:\\n\", cm)\n", + " print(f\"Accuracy : {acc:.4f}\")\n", + " print(f\"AUC : {auc:.4f}\")\n", + " print(f\"TP={tp}, TN={tn}, FP={fp}, FN={fn}\")\n", + "\n", + " hasil.append({\n", + " \"Model\": nama,\n", + " \"Accuracy\": acc,\n", + " \"AUC\": auc,\n", + " \"TP\": tp,\n", + " \"TN\": tn,\n", + " \"FP\": fp,\n", + " \"FN\": fn,\n", + " \"Model_Obj\": model\n", + " })\n", + "\n", + " # Simpan data untuk ROC Curve\n", + " fpr, tpr, _ = roc_curve(y_test, y_prob)\n", + " roc_data[nama] = (fpr, tpr, auc)\n", + "\n", + "# ===============================================\n", + "# 3. PILIH MODEL TERBAIK DAN SIMPAN\n", + "# ===============================================\n", + "df_hasil = pd.DataFrame(hasil)[[\"Model\", \"Accuracy\", \"AUC\", \"TP\", \"TN\", \"FP\", \"FN\"]]\n", + "best = max(hasil, key=lambda x: x[\"Accuracy\"])\n", + "best_model = best[\"Model_Obj\"]\n", + "best_name = best[\"Model\"]\n", + "\n", + "print(f\"\\nModel terbaik berdasarkan akurasi: {best_name}\")\n", + "\n", + "# Simpan model dan nama fitur\n", + "joblib.dump(best_model, \"kelompok.pkl\")\n", + "joblib.dump(list(X.columns), \"feature_names.pkl\")\n", + "print(\"Model terbaik disimpan ke 'kelompok.pkl'\")\n", + "\n", + "# ===============================================\n", + "# 4. SIMPAN HASIL KE EXCEL\n", + "# ===============================================\n", + "df_hasil.to_excel(\"hasil_perbandingan.xlsx\", index=False)\n", + "print(\"Hasil evaluasi disimpan ke 'hasil_perbandingan.xlsx'\")\n", + "\n", + "# ===============================================\n", + "# 5. PLOT DAN SIMPAN ROC CURVE\n", + "# ===============================================\n", + "plt.figure(figsize=(8, 6))\n", + "for nama, (fpr, tpr, auc) in roc_data.items():\n", + " plt.plot(fpr, tpr, label=f\"{nama} (AUC = {auc:.2f})\")\n", + "\n", + "plt.plot([0, 1], [0, 1], '--', color='gray')\n", + "plt.title(\"Perbandingan ROC Curve Tiga Model\")\n", + "plt.xlabel(\"False Positive Rate\")\n", + "plt.ylabel(\"True Positive Rate\")\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.tight_layout()\n", + "plt.savefig(\"roc_curve.png\")\n", + "plt.show()\n", + "\n", + "print(\"\\nGrafik ROC Curve disimpan sebagai 'roc_curve.png'\")\n", + "\n", + "# ===============================================\n", + "# 6. CONTOH PREDIKSI MENGGUNAKAN MODEL\n", + "# ===============================================\n", + "print(\"\\n=== Contoh Prediksi Menggunakan Model ===\")\n", + "mdl = joblib.load(\"kelompok.pkl\")\n", + "feature_names = joblib.load(\"feature_names.pkl\")\n", + "\n", + "# Tampilkan nama kolom untuk panduan\n", + "print(\"Kolom fitur yang digunakan:\", feature_names)\n", + "\n", + "print(f\"\")\n", + "\n", + "# Contoh data input manual\n", + "print(f\"Ini yang sehat\")\n", + "sample = pd.DataFrame([{\n", + " \"age\": 52,\n", + " \"sex\": 1,\n", + " \"cp\": 0,\n", + " \"trestbps\": 125,\n", + " \"chol\": 212,\n", + " \"fbs\": 0,\n", + " \"restecg\": 1,\n", + " \"thalach\": 168,\n", + " \"exang\": 0,\n", + " \"oldpeak\": 1.0,\n", + " \"slope\": 2,\n", + " \"ca\": 2,\n", + " \"thal\": 3\n", + "}])\n", + "\n", + "# Pastikan urutan kolom sesuai\n", + "sample = sample[feature_names]\n", + "\n", + "pred = int(mdl.predict(sample)[0])\n", + "prob = mdl.predict_proba(sample)[0][1]\n", + "\n", + "print(f\"\\nPrediksi: {pred} ({'Berisiko penyakit jantung' if pred==1 else 'Sehat'})\")\n", + "print(f\"Probabilitas risiko: {prob*100:.2f}%\")\n", + "\n", + "print(f\"\")\n", + "\n", + "# Contoh data input manual\n", + "print(f\"Ini yang berisiko penyakit jantung\")\n", + "sample = pd.DataFrame([{\n", + " \"age\": 38,\n", + " \"sex\": 1, \n", + " \"cp\": 2, \n", + " \"trestbps\": 138, \n", + " \"chol\": 175, \n", + " \"fbs\": 0, \n", + " \"restecg\": 1, \n", + " \"thalach\": 1173, \n", + " \"exang\": 0, \n", + " \"oldpeak\": 0, \n", + " \"slope\": 2, \n", + " \"ca\": 4, \n", + " \"thal\": 2 \n", + "}])\n", + "sample = sample[feature_names]\n", + "\n", + "pred = int(mdl.predict(sample)[0])\n", + "prob = mdl.predict_proba(sample)[0][1]\n", + "\n", + "print(f\"\\nPrediksi: {pred} ({'Berisiko penyakit jantung' if pred==1 else 'Sehat'})\")\n", + "print(f\"Probabilitas risiko: {prob*100:.2f}%\")\n", + "\n", + "# ===============================================\n", + "# 7. HITUNG TP, TN, FP, FN DARI MODEL DAN DATASET ASLI\n", + "# ===============================================\n", + "print(\"\\n=== Perhitungan TP, TN, FP, FN Manual ===\")\n", + "\n", + "# Load ulang dataset dan model\n", + "df = pd.read_csv(\"heart.csv\")\n", + "X = df.drop(\"target\", axis=1)\n", + "y = df[\"target\"]\n", + "\n", + "mdl = joblib.load(\"kelompok.pkl\")\n", + "\n", + "# Prediksi semua data\n", + "y_pred = mdl.predict(X)\n", + "\n", + "# Hitung confusion matrix\n", + "cm = confusion_matrix(y, y_pred)\n", + "tn, fp, fn, tp = cm.ravel()\n", + "\n", + "print(\"Confusion Matrix:\")\n", + "print(cm)\n", + "print(f\"True Negative (TN): {tn}\")\n", + "print(f\"False Positive (FP): {fp}\")\n", + "print(f\"False Negative (FN): {fn}\")\n", + "print(f\"True Positive (TP): {tp}\")\n", + "\n", + "# Simpan hasil manual ke Excel (sheet baru)\n", + "with pd.ExcelWriter(\"hasil_perbandingan.xlsx\", mode=\"a\", engine=\"openpyxl\") as writer:\n", + " df_cm = pd.DataFrame([[tn, fp, fn, tp]],\n", + " columns=[\"TN\", \"FP\", \"FN\", \"TP\"])\n", + " df_cm.to_excel(writer, sheet_name=\"Confusion_Manual\", index=False)\n", + "\n", + "print(\"Hasil TP, TN, FP, FN disimpan ke sheet 'Confusion_Manual' di hasil_perbandingan.xlsx\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv (3.10.11)", + "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": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 1bc4bf8a96c656e028fefd8715ad56276e92b5ee Mon Sep 17 00:00:00 2001 From: Kavic Date: Thu, 20 Nov 2025 23:37:02 +0700 Subject: [PATCH 3/5] feat: Tugas kelompok, Accuracy, Precision, Recall, F1-Score --- hasil_perbandingan.xlsx | Bin 5691 -> 5825 bytes kelompok.ipynb | 76 ++++++++++++++++++++++++---------------- kelompok.py | 65 +++++++++++++++++++--------------- 3 files changed, 82 insertions(+), 59 deletions(-) diff --git a/hasil_perbandingan.xlsx b/hasil_perbandingan.xlsx index 645fc127e6bdbc1bde3b72f6063cf60e958a666d..fe775c06283b1ec94717b59cd980d9fdb3244fdf 100644 GIT binary patch delta 1374 zcmdn3b5NHzz?+#xgn@y9gF$O&$wb}*^*}1x>dA4@w?M(uj0_BdKsqHqIiM)NpjbaS zzbI9&A~$F1MBnVg20U&5y*}DboV)3e&#cqO{G@rpZrnI~BP(01J|SD>?|1J_X1Oo* ztfSs9b{AS#I>kz*##b>(sk`#x?rZ5QAJ2N*xrpUNRlncUB85K!dCo;$ZRxihL+XEd zzZK9r7ye+vl)xnkB4UMRn-*Vs5h=Mp*qXD4E$8CG_}8*l-EC>fB94E*O^nUpO|$kW2LYb*GftKuJ(OvA1;cJPya7=L8py#5})Ap zH&gcL%gdSjuUT198qN_IQllI;>Hg`i`o49m75{C%z?!`M+kCgeL>taoQhEQ@{$xZ6 zq|GlG?b!H0!E_+(QqB%028O*HlNWGFPF^6wTHhKLSiHzU;GZ_n`*05q0jm=}SEEYT znfK+spBLP5N~A+PY45+^YFCs_D(*X?a=&i&-M?l>ew_Fby?1d)#Q#%^R=I!k>-f9& z@s*c-)0XT@td8_FZ`orl_ocqmuV6%%=0Eqe!J|_?=b! 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PILIH MODEL TERBAIK DAN SIMPAN\n", "# ===============================================\n", - "df_hasil = pd.DataFrame(hasil)[[\"Model\", \"Accuracy\", \"AUC\", \"TP\", \"TN\", \"FP\", \"FN\"]]\n", + "df_hasil = pd.DataFrame(hasil)[[\n", + " \"Model\", \"Accuracy\", \"Precision\", \"Recall\", \"F1-Score\", \"AUC\",\n", + " \"TP\", \"TN\", \"FP\", \"FN\"\n", + "]]\n", + "\n", "best = max(hasil, key=lambda x: x[\"Accuracy\"])\n", "best_model = best[\"Model_Obj\"]\n", "best_name = best[\"Model\"]\n", @@ -212,7 +234,6 @@ "mdl = joblib.load(\"kelompok.pkl\")\n", "feature_names = joblib.load(\"feature_names.pkl\")\n", "\n", - "# Tampilkan nama kolom untuk panduan\n", "print(\"Kolom fitur yang digunakan:\", feature_names)\n", "\n", "print(f\"\")\n", @@ -235,7 +256,6 @@ " \"thal\": 3\n", "}])\n", "\n", - "# Pastikan urutan kolom sesuai\n", "sample = sample[feature_names]\n", "\n", "pred = int(mdl.predict(sample)[0])\n", @@ -246,23 +266,24 @@ "\n", "print(f\"\")\n", "\n", - "# Contoh data input manual\n", + "# Contoh data kedua\n", "print(f\"Ini yang berisiko penyakit jantung\")\n", "sample = pd.DataFrame([{\n", " \"age\": 38,\n", - " \"sex\": 1, \n", - " \"cp\": 2, \n", - " \"trestbps\": 138, \n", - " \"chol\": 175, \n", - " \"fbs\": 0, \n", - " \"restecg\": 1, \n", - " \"thalach\": 1173, \n", - " \"exang\": 0, \n", - " \"oldpeak\": 0, \n", - " \"slope\": 2, \n", - " \"ca\": 4, \n", - " \"thal\": 2 \n", + " \"sex\": 1,\n", + " \"cp\": 2,\n", + " \"trestbps\": 138,\n", + " \"chol\": 175,\n", + " \"fbs\": 0,\n", + " \"restecg\": 1,\n", + " \"thalach\": 173,\n", + " \"exang\": 0,\n", + " \"oldpeak\": 0,\n", + " \"slope\": 2,\n", + " \"ca\": 4,\n", + " \"thal\": 2\n", "}])\n", + "\n", "sample = sample[feature_names]\n", "\n", "pred = int(mdl.predict(sample)[0])\n", @@ -276,17 +297,14 @@ "# ===============================================\n", "print(\"\\n=== Perhitungan TP, TN, FP, FN Manual ===\")\n", "\n", - "# Load ulang dataset dan model\n", "df = pd.read_csv(\"heart.csv\")\n", "X = df.drop(\"target\", axis=1)\n", "y = df[\"target\"]\n", "\n", "mdl = joblib.load(\"kelompok.pkl\")\n", "\n", - "# Prediksi semua data\n", "y_pred = mdl.predict(X)\n", "\n", - "# Hitung confusion matrix\n", "cm = confusion_matrix(y, y_pred)\n", "tn, fp, fn, tp = cm.ravel()\n", "\n", @@ -297,10 +315,8 @@ "print(f\"False Negative (FN): {fn}\")\n", "print(f\"True Positive (TP): {tp}\")\n", "\n", - "# Simpan hasil manual ke Excel (sheet baru)\n", "with pd.ExcelWriter(\"hasil_perbandingan.xlsx\", mode=\"a\", engine=\"openpyxl\") as writer:\n", - " df_cm = pd.DataFrame([[tn, fp, fn, tp]],\n", - " columns=[\"TN\", \"FP\", \"FN\", \"TP\"])\n", + " df_cm = pd.DataFrame([[tn, fp, fn, tp]], columns=[\"TN\", \"FP\", \"FN\", \"TP\"])\n", " df_cm.to_excel(writer, sheet_name=\"Confusion_Manual\", index=False)\n", "\n", "print(\"Hasil TP, TN, FP, FN disimpan ke sheet 'Confusion_Manual' di hasil_perbandingan.xlsx\")\n" diff --git a/kelompok.py b/kelompok.py index d032faa..743eb01 100644 --- a/kelompok.py +++ b/kelompok.py @@ -1,17 +1,15 @@ # =============================================== -# TUGAS : Machine Learning - Perbandingan 3 Algoritma -# DATA : Heart Disease Dataset (heart.csv) +# KELAS: 05TPLE016 +# KELOMPOK 1 +# TUGAS : Machine Learning - 3 Algoritma # =============================================== -import warnings -warnings.filterwarnings('ignore') - import pandas as pd import numpy as np import matplotlib.pyplot as plt import joblib from sklearn.model_selection import train_test_split -from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve +from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve, precision_score, recall_score, f1_score from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import LogisticRegression @@ -52,19 +50,30 @@ cm = confusion_matrix(y_test, y_pred) tn, fp, fn, tp = cm.ravel() - + acc = accuracy_score(y_test, y_pred) auc = roc_auc_score(y_test, y_prob) + # === Tambahan: Precision, Recall, F1 === + precision = precision_score(y_test, y_pred) + recall = recall_score(y_test, y_pred) + f1 = f1_score(y_test, y_pred) + print(f"\n=== {nama} ===") print("Confusion Matrix:\n", cm) print(f"Accuracy : {acc:.4f}") + print(f"Precision: {precision:.4f}") + print(f"Recall : {recall:.4f}") + print(f"F1-Score : {f1:.4f}") print(f"AUC : {auc:.4f}") print(f"TP={tp}, TN={tn}, FP={fp}, FN={fn}") hasil.append({ "Model": nama, "Accuracy": acc, + "Precision": precision, + "Recall": recall, + "F1-Score": f1, "AUC": auc, "TP": tp, "TN": tn, @@ -80,7 +89,11 @@ # =============================================== # 3. PILIH MODEL TERBAIK DAN SIMPAN # =============================================== -df_hasil = pd.DataFrame(hasil)[["Model", "Accuracy", "AUC", "TP", "TN", "FP", "FN"]] +df_hasil = pd.DataFrame(hasil)[[ + "Model", "Accuracy", "Precision", "Recall", "F1-Score", "AUC", + "TP", "TN", "FP", "FN" +]] + best = max(hasil, key=lambda x: x["Accuracy"]) best_model = best["Model_Obj"] best_name = best["Model"] @@ -124,7 +137,6 @@ mdl = joblib.load("kelompok.pkl") feature_names = joblib.load("feature_names.pkl") -# Tampilkan nama kolom untuk panduan print("Kolom fitur yang digunakan:", feature_names) print(f"") @@ -147,7 +159,6 @@ "thal": 3 }]) -# Pastikan urutan kolom sesuai sample = sample[feature_names] pred = int(mdl.predict(sample)[0]) @@ -158,23 +169,24 @@ print(f"") -# Contoh data input manual +# Contoh data kedua print(f"Ini yang berisiko penyakit jantung") sample = pd.DataFrame([{ "age": 38, - "sex": 1, - "cp": 2, - "trestbps": 138, - "chol": 175, - "fbs": 0, - "restecg": 1, - "thalach": 1173, - "exang": 0, - "oldpeak": 0, - "slope": 2, - "ca": 4, - "thal": 2 + "sex": 1, + "cp": 2, + "trestbps": 138, + "chol": 175, + "fbs": 0, + "restecg": 1, + "thalach": 173, + "exang": 0, + "oldpeak": 0, + "slope": 2, + "ca": 4, + "thal": 2 }]) + sample = sample[feature_names] pred = int(mdl.predict(sample)[0]) @@ -188,17 +200,14 @@ # =============================================== print("\n=== Perhitungan TP, TN, FP, FN Manual ===") -# Load ulang dataset dan model df = pd.read_csv("heart.csv") X = df.drop("target", axis=1) y = df["target"] mdl = joblib.load("kelompok.pkl") -# Prediksi semua data y_pred = mdl.predict(X) -# Hitung confusion matrix cm = confusion_matrix(y, y_pred) tn, fp, fn, tp = cm.ravel() @@ -209,10 +218,8 @@ print(f"False Negative (FN): {fn}") print(f"True Positive (TP): {tp}") -# Simpan hasil manual ke Excel (sheet baru) with pd.ExcelWriter("hasil_perbandingan.xlsx", mode="a", engine="openpyxl") as writer: - df_cm = pd.DataFrame([[tn, fp, fn, tp]], - columns=["TN", "FP", "FN", "TP"]) + df_cm = pd.DataFrame([[tn, fp, fn, tp]], columns=["TN", "FP", "FN", "TP"]) df_cm.to_excel(writer, sheet_name="Confusion_Manual", index=False) print("Hasil TP, TN, FP, FN disimpan ke sheet 'Confusion_Manual' di hasil_perbandingan.xlsx") From ec9e650659a6707a127ff39d926f1775fafed716 Mon Sep 17 00:00:00 2001 From: Kavic Date: Fri, 21 Nov 2025 23:00:02 +0700 Subject: [PATCH 4/5] feat: Update --- hasil_perbandingan.xlsx | Bin 5825 -> 5827 bytes kelompok.ipynb | 10 +++++----- kelompok.py | 6 +++--- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/hasil_perbandingan.xlsx b/hasil_perbandingan.xlsx index fe775c06283b1ec94717b59cd980d9fdb3244fdf..66f3aa27c75434b2f04ec96a3f9ba0d14487c2cf 100644 GIT binary patch delta 953 zcmX@8dsvq@z?+#xgn@y9gP~-5=|tWGfZeXT=y%wF zr!7A9)ZWs|K}%dC^%eXS6b(6#=z8s0yC`$fkrj7uPoB0`x{3SvkN(piD zy0^K0C(G`Y&0A!$Xq#xx(zn09Y?j@`%9b3ysqe{o_fLOZzHLypQb}LiGyA5K$(gr0 zzOgqOroHQmGT0Y3`Rb(FtnZpvO{Z;GwCQq!#o5g{v!VhfP3+0uP`YUO-}a|b+?$Gc z8M3b~-r}Mx_W9t=)xPzY3f*{8D>PR)-c*`%$KTmwva;2kg--u;Tx_%jI^CM3^aDhd zggdied~p!dPl;INo9VgI$>-N0F`eX}-uGB$zb|^e=upvju{6CSCZ7*Tt$ln$@aLu3 zHck2OHut^c6K#(5Y^(N;?M{<7tm`Q;agRAFAgpXEq7<~4UH*FL`*z#V43oKY7wX?A zy{dft`t+1n=YCGDwej3mA=miH-l6h+u0e=nFWaj0{*r|ct_T0x%7YJI-i_B!6V zLEh{4&TyY}^?S?V?>hS!P@-e<55bq&9sz-kVJUCPc6pG~?UJzG9kSUL}xsbMjO%H8xFdA4@w-c|))%#{2HsERd@Ac7k;@nM# zd}f_K<|oY)cH_p`8(G<6^$FQ3f4_TgGRu9bXC3u^vAfW^(kWIdHNJ{TO5K$gcVA0i z`FPgb&P6OIs`~w&7AgD@$a600YD>T681l>et$^0K@COs71TIMs5i2y?wD{7CNXh-d z)|@?TITsf$=UF6Dkj7a*`EK)ceU?Z`*2Z&eENT}3p#C_llTO$znQW}UtZ4Kf6dB@(r}KzkQ(K%N%v28 z)%UGi{crOH*5vKq=DQUp+HlU2%KNwWCnHKoM9++wqb#ubGou9?GcZIp>v6nh0yAW| zS22PaCwb0;MJCVYe+CY;Ggs6&JSKMu$kzJ-1FbcF=_%$5>zX`$w9RKXc>d{Jcz4m3 z((r807}a-d)m4*1TD?3vw2vP@H$6GMQmI;_lka4E*fHH4#i(a_6PK;aa`Y~Dl$D6j zK9P0OZtL$Uv)HsFg0E&9%y|}R=C7-9(&O0H8uitmrFA3oDMBVF#sf>If4rTD7J?jO!NUo*bDC}~6X z$%6Ei?=QSjKGr|c?^(xciR7duZB8y(UmiSOCiiCpdx-C{&D>vDcdulB^5s{T&t>cV zo4&heR;^T9@P|LZu0opk>V*Z3zfLO0YW(;b@-N@1W7?PDBNFT68WA&jP|^j*8yf?|9CZc;VPLw3fdx?4 Date: Sun, 14 Dec 2025 11:13:53 +0700 Subject: [PATCH 5/5] feat: Add feature machine learning kelompok, correlation heatmap, distribution heatmap, penjelasan komponen --- ... 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Grafik distribusi target disimpan sebagai 'distribusi_target.png'\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Heatmap korelasi disimpan sebagai 'correlation_heatmap.png'\n", "\n", "=== Decision Tree ===\n", "Confusion Matrix:\n", @@ -105,6 +139,7 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import joblib\n", + "import seaborn as sns\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve, precision_score, recall_score, f1_score\n", "from sklearn.tree import DecisionTreeClassifier\n", @@ -129,7 +164,59 @@ ")\n", "\n", "# ===============================================\n", - "# 2. INISIALISASI 3 MODEL DAN LATIH\n", + "# 2. DISTRIBUSI TARGET\n", + "# ===============================================\n", + "\n", + "plt.figure(figsize=(6, 5))\n", + "\n", + "ax = sns.countplot(x=\"target\", data=df)\n", + "\n", + "# Tambahkan label jumlah di atas bar\n", + "for p in ax.patches:\n", + " ax.annotate(\n", + " f\"{int(p.get_height())}\",\n", + " (p.get_x() + p.get_width() / 2., p.get_height()),\n", + " ha=\"center\",\n", + " va=\"bottom\"\n", + " )\n", + "\n", + "plt.title(\"Distribusi Target Penyakit Jantung\")\n", + "plt.xlabel(\"Target (0 = Sehat, 1 = Berisiko)\")\n", + "plt.ylabel(\"Jumlah Data\")\n", + "plt.tight_layout()\n", + "plt.savefig(\"distribusi_target.png\")\n", + "plt.show()\n", + "\n", + "print(\"Grafik distribusi target disimpan sebagai 'distribusi_target.png'\")\n", + "\n", + "# ===============================================\n", + "# 3. KORELASI FITUR (HEATMAP)\n", + "# ===============================================\n", + "\n", + "plt.figure(figsize=(12, 10))\n", + "\n", + "# Hitung matriks korelasi\n", + "corr_matrix = df.corr()\n", + "\n", + "# Plot heatmap\n", + "sns.heatmap(\n", + " corr_matrix,\n", + " annot=True,\n", + " fmt=\".2f\",\n", + " cmap=\"coolwarm\",\n", + " linewidths=0.5\n", + ")\n", + "\n", + "plt.title(\"Heatmap Korelasi Antar Fitur Dataset Heart Disease\")\n", + "plt.tight_layout()\n", + "plt.savefig(\"correlation_heatmap.png\")\n", + "plt.show()\n", + "\n", + "print(\"Heatmap korelasi disimpan sebagai 'correlation_heatmap.png'\")\n", + "\n", + "\n", + "# ===============================================\n", + "# 4. INISIALISASI 3 MODEL DAN LATIH\n", "# ===============================================\n", "models = {\n", " \"Decision Tree\": DecisionTreeClassifier(random_state=42),\n", @@ -184,7 +271,7 @@ " roc_data[nama] = (fpr, tpr, auc)\n", "\n", "# ===============================================\n", - "# 3. PILIH MODEL TERBAIK DAN SIMPAN\n", + "# 5. PILIH MODEL TERBAIK DAN SIMPAN\n", "# ===============================================\n", "df_hasil = pd.DataFrame(hasil)[[\n", " \"Model\", \"Accuracy\", \"Precision\", \"Recall\", \"F1-Score\", \"AUC\",\n", @@ -203,13 +290,13 @@ "print(\"Model terbaik disimpan ke 'kelompok.pkl'\")\n", "\n", "# ===============================================\n", - "# 4. SIMPAN HASIL KE EXCEL\n", + "# 6. SIMPAN HASIL KE EXCEL\n", "# ===============================================\n", "df_hasil.to_excel(\"hasil_perbandingan.xlsx\", index=False)\n", "print(\"Hasil evaluasi disimpan ke 'hasil_perbandingan.xlsx'\")\n", "\n", "# ===============================================\n", - "# 5. PLOT DAN SIMPAN ROC CURVE\n", + "# 7. PLOT DAN SIMPAN ROC CURVE\n", "# ===============================================\n", "plt.figure(figsize=(8, 6))\n", "for nama, (fpr, tpr, auc) in roc_data.items():\n", @@ -228,7 +315,7 @@ "print(\"\\nGrafik ROC Curve disimpan sebagai 'roc_curve.png'\")\n", "\n", "# ===============================================\n", - "# 6. CONTOH PREDIKSI MENGGUNAKAN MODEL\n", + "# 8. CONTOH PREDIKSI MENGGUNAKAN MODEL\n", "# ===============================================\n", "print(\"\\n=== Contoh Prediksi Menggunakan Model ===\")\n", "mdl = joblib.load(\"kelompok.pkl\")\n", @@ -293,7 +380,7 @@ "print(f\"Probabilitas risiko: {prob*100:.2f}%\")\n", "\n", "# ===============================================\n", - "# 7. HITUNG TP, TN, FP, FN DARI MODEL DAN DATASET ASLI\n", + "# 9. HITUNG TP, TN, FP, FN DARI MODEL DAN DATASET ASLI\n", "# ===============================================\n", "print(\"\\n=== Hasil TP, TN, FP, FN ===\")\n", "\n", diff --git a/kelompok.py b/kelompok.py index d6b1062..503c4e5 100644 --- a/kelompok.py +++ b/kelompok.py @@ -8,6 +8,7 @@ import numpy as np import matplotlib.pyplot as plt import joblib +import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve, precision_score, recall_score, f1_score from sklearn.tree import DecisionTreeClassifier @@ -32,7 +33,59 @@ ) # =============================================== -# 2. INISIALISASI 3 MODEL DAN LATIH +# 2. DISTRIBUSI TARGET +# =============================================== + +plt.figure(figsize=(6, 5)) + +ax = sns.countplot(x="target", data=df) + +# Tambahkan label jumlah di atas bar +for p in ax.patches: + ax.annotate( + f"{int(p.get_height())}", + (p.get_x() + p.get_width() / 2., p.get_height()), + ha="center", + va="bottom" + ) + +plt.title("Distribusi Target Penyakit Jantung") +plt.xlabel("Target (0 = Sehat, 1 = Berisiko)") +plt.ylabel("Jumlah Data") +plt.tight_layout() +plt.savefig("distribusi_target.png") +plt.show() + +print("Grafik distribusi target disimpan sebagai 'distribusi_target.png'") + +# =============================================== +# 3. KORELASI FITUR (HEATMAP) +# =============================================== + +plt.figure(figsize=(12, 10)) + +# Hitung matriks korelasi +corr_matrix = df.corr() + +# Plot heatmap +sns.heatmap( + corr_matrix, + annot=True, + fmt=".2f", + cmap="coolwarm", + linewidths=0.5 +) + +plt.title("Heatmap Korelasi Antar Fitur Dataset Heart Disease") +plt.tight_layout() +plt.savefig("correlation_heatmap.png") +plt.show() + +print("Heatmap korelasi disimpan sebagai 'correlation_heatmap.png'") + + +# =============================================== +# 4. INISIALISASI 3 MODEL DAN LATIH # =============================================== models = { "Decision Tree": DecisionTreeClassifier(random_state=42), @@ -87,7 +140,7 @@ roc_data[nama] = (fpr, tpr, auc) # =============================================== -# 3. PILIH MODEL TERBAIK DAN SIMPAN +# 5. PILIH MODEL TERBAIK DAN SIMPAN # =============================================== df_hasil = pd.DataFrame(hasil)[[ "Model", "Accuracy", "Precision", "Recall", "F1-Score", "AUC", @@ -106,13 +159,13 @@ print("Model terbaik disimpan ke 'kelompok.pkl'") # =============================================== -# 4. SIMPAN HASIL KE EXCEL +# 6. SIMPAN HASIL KE EXCEL # =============================================== df_hasil.to_excel("hasil_perbandingan.xlsx", index=False) print("Hasil evaluasi disimpan ke 'hasil_perbandingan.xlsx'") # =============================================== -# 5. PLOT DAN SIMPAN ROC CURVE +# 7. PLOT DAN SIMPAN ROC CURVE # =============================================== plt.figure(figsize=(8, 6)) for nama, (fpr, tpr, auc) in roc_data.items(): @@ -131,7 +184,7 @@ print("\nGrafik ROC Curve disimpan sebagai 'roc_curve.png'") # =============================================== -# 6. CONTOH PREDIKSI MENGGUNAKAN MODEL +# 8. CONTOH PREDIKSI MENGGUNAKAN MODEL # =============================================== print("\n=== Contoh Prediksi Menggunakan Model ===") mdl = joblib.load("kelompok.pkl") @@ -196,7 +249,7 @@ print(f"Probabilitas risiko: {prob*100:.2f}%") # =============================================== -# 7. HITUNG TP, TN, FP, FN DARI MODEL DAN DATASET ASLI +# 9. HITUNG TP, TN, FP, FN DARI MODEL DAN DATASET ASLI # =============================================== print("\n=== Hasil TP, TN, FP, FN ===")