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All Journal JURIKOM (Jurnal Riset Komputer)
Dwi Martantiningsih
Universitas PGRI Ronggolawe, Tuban
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Prediksi Persetujuan Pinjaman Bank Ritel Menggunakan CatBoost dengan Optimasi Hyperparameter Berbasis Optuna dan Analisis Interpretabilitas SHAP Dwi Martantiningsih; Andy Haryoko; Amaludin Arifia
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9790

Abstract

The banking sector faces significant challenges in accurately classifying loan applications, particularly for datasets dominated by categorical features with imbalanced class distribution (71%:29%). This study proposes the application of CatBoost (Categorical Boosting) with Optuna-based hyperparameter optimization a Bayesian optimization framework using Tree-structured Parzen Estimator (TPE) for bank loan approval prediction. Two class imbalance handling scenarios are comparatively evaluated: SMOTE and CatBoost built-in class_weight. Experiments are conducted on a dataset of 381 samples with 15 active features (12 original features and 3 engineered features) using 5-fold stratified cross-validation. Results show LightGBM achieves the best overall performance with Accuracy 93.42%, Precision 94.83%, Recall 96.49%, F1-Score 95.65%, ROC-AUC 0.9215, and MCC 0.8220. CatBoost (SMOTE) achieves competitive performance with AUC-CV 0.9030 and F1 94.02%. SHAP (SHapley Additive exPlanations) analysis identifies Credit_History as the dominant feature (mean|SHAP|=3.2225), followed by ApplicantIncome (0.6896) and Property_Area_Semiurban (0.5222). This study contributes as the first investigation integrating CatBoost+Optuna+XAI-SHAP in retail bank loan approval prediction with dominant categorical features, while providing systematic comparison against LightGBM, XGBoost, and Random Forest.
Co-Authors Amaludin Arifia Andy Haryoko