Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Advances in Data and Information Systems
Eni Sumarminingsih
Department of Statistics, Brawijaya University, Malang, Indonesia
Computer Science & IT Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
AugLog-LightGBM: A Log-Based Feature AugmentationFramework for Class Imbalance in Credit RiskClassification Hana Azizah; Eni Sumarminingsih; Adji Achmad Rinaldo Fernandes
International Journal of Advances in Data and Information Systems Vol. 7 No. 2 (2026): August 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i2.1623

Abstract

Non-performing loan (NPL) detection is inherently a class-imbalance problem because defaulting borrowers represent a persistent minority. Standard gradient boosting often favors the majority class. This paper proposes AugLog-LightGBM, an extension of LightGBM that improves initialization through Log-Based Feature Augmentation (LBFA). Instead of using an uninformative constant, boosting starts from an informed prior combining a logistic-regression logit score and a kernel-density-estimation log-density ratio (LDR), which capture complementary global and local information. These representations are incorporated as augmented features and as the init_score, reformulating boosting as residual correction over an informed Bayesian prior. The proposed framework is evaluated on a dataset of 2,700 home-mortgage borrowers collected from partner banks in Malang, Indonesia (NPL rate = 16.11%), using repeated stratified cross-validation and comparison against four imbalance-aware baselines. AugLog-LightGBM achieves the highest ROC-AUC (0.815 ± 0.018), PR-AUC (0.679), and F1-score (0.631). DeLong tests show statistically significant ROC-AUC improvements over class-weighted Logistic Regression and Random Forest, while gains over XGBoost and SMOTE + LightGBM are positive but not statistically significant. Robustness analyses and SHAP interpretation further support the consistency and practical applicability of the proposed framework.
Co-Authors Adji Achmad Rinaldo Fernandes Hana Azizah