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All Journal JURIKOM (Jurnal Riset Komputer)
Amaludin Arifia
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.
Perbandingan Moving Average dan Exponential Smoothing untuk Prediksi Harga Saham BBRI pada Dataset 2019–2026 Wahyu Dedy Setiyawan; 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.v13i3.9791

Abstract

This study compares four time series forecasting methods Simple Moving Average (SMA), Double Moving Average (DMA), Single Exponential Smoothing (SES), and Double Exponential Smoothing (DES/Holt) for predicting the closing stock price of BBRI.JK. The dataset comprises 1,768 daily observations spanning January 2019 to December 2026, split into training (80%) and testing (20%) sets. Each method's parameters were optimized via grid search minimizing MAPE, then evaluated across three metrics: MAPE, MAE, and RMSE. SES (α = 0.9) emerged as the best-performing model, achieving a MAPE of 0.3763%, MAE of IDR 14.93, and RMSE of IDR 24.31 substantially outperforming SMA (3.1591%), DMA (2.7561%), and DES (3.6973%). These findings offer methodological guidance for researchers and practical insight for investors operating in emerging market equities with near weak-form efficiency.
Co-Authors Andy Haryoko Dwi Martantiningsih Wahyu Dedy Setiyawan