Deteksi Penipuan Kartu Kredit Melalui Pendekatan Stacking Ensemble Berbasis Gradient Boosting dengan Optimasi Hyperparameter

SHAFA SYAHIDA, DONNA NUR TAMARA, DIAH SEPTIANI

Abstract


Deteksi fraud kartu kredit merupakan tantangan penting karena data transaksi memiliki ketidakseimbangan kelas yang tinggi. Penelitian ini membangun model Stacking Ensemble berbasis Gradient Boosting dengan XGBoost, LightGBM, dan CatBoost sebagai base learner, serta Logistic Regression sebagai meta-learner. Tahapan penelitian meliputi preprocessing menggunakan RobustScaler, feature selection berbasis SHAP, penanganan ketidakseimbangan kelas menggunakan SMOTE, dan optimasi hyperparameter menggunakan Optuna. Hasil penelitian menunjukkan bahwa Stacking Ensemble menghasilkan performa terbaik dengan precision 0,7798, recall 0,8673, F1-Score 0,8213, ROC-AUC 0,9789, dan PR-AUC 0,8534. Hasil ini menunjukkan bahwa penggabungan beberapa model gradient boosting mampu menghasilkan keseimbangan precision dan recall yang lebih baik dibandingkan model individual. 

Keywords


Credit card fraud detection; hyperparameter tuning; logistic regression; SMOTE; stacking ensemble

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