Resampling And Cost-Sensitive Random Forest and Xgboost For Non-Performing Sharia Financing Prediction
DOI:
https://doi.org/10.59261/jequi.v8i5.467Abstract
Background: Class imbalance presents a significant challenge in predicting non-performing financing (NPF) in Islamic financial institutions, potentially reducing the effectiveness of minority-class detection.
Objective: This study aims to compare six resampling techniques combined with Random Forest and XGBoost under standard and cost-sensitive learning to identify effective configurations for NPF prediction.
Methods: A quantitative experimental approach was applied to 8,814 historical Sharia financing records from 2016 to 2025. Six resampling techniques—SMOTE, ADASYN, Borderline-SMOTE, SMOTE-ENN, Distributed Gaussian (D-GA), and Distributed Poisson (D-PO)—were evaluated using stratified cross-validation. Model performance was assessed using the area under the precision–recall curve (AUPRC), macro F1 score, NPF precision, and NPF recall, followed by evaluation on a reported holdout dataset of 1,763 records.
Results: D-GA combined with cost-sensitive XGBoost achieved the highest cross-validation AUPRC (0.5503). On the holdout dataset, Borderline-SMOTE combined with standard XGBoost achieved the highest AUPRC (0.5942) and NPF precision (100.00%), although NPF recall remained 27.75%. SMOTE-ENN combined with cost-sensitive XGBoost achieved the highest NPF recall (43.54%), whereas SMOTE combined with standard Random Forest achieved the highest macro F1 score (0.7089).
Conclusion: Different configurations demonstrate distinct trade-offs between NPF detection and prediction reliability. The findings support objective-specific model selection, although inconsistent class distributions, unavailable baseline results, and data from a single institution limit generalizability.
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References
Abedin, T., Xu, H., & Uddin, S. (2026). The impact of K selection in K‑fold cross-validation on bias and variance in supervised learning models. Scientific Reports, 16(1), 6084. https://doi.org/10.1038/s41598-026-37247-x
Afane, K., & Zhao, Y. (2024). Selecting Classifiers and Resampling Techniques for Imbalanced Datasets: A New Perspective. Procedia Computer Science, 246, 1150–1159. https://doi.org/10.1016/j.procs.2024.09.539
Ahmad, M., Prabowo, H., Warnars, H. L. H. S., & Gaol, F. L. (2023). A Machine Learning Approach for Model Selection of Social Aid Beneficiaries. Journal of System and Management Sciences, 13(6), 230–243. https://doi.org/10.33168/JSMS.2023.0614
Albright Wiladi, J., & Mauritsius, T. (2026). INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION journal homepage : www.joiv.org/index.php/joiv INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION Forecasting the Demand of a Specific Water Pump Product with a 3-day Lead Time. JOIV : Int. J. Inform. Visualization, 10. www.joiv.org/index.php/joiv
Alvi, J., Arif, I., & Nizam, K. (2024). Advancing financial resilience: A systematic review of default prediction models and future directions in credit risk management. Heliyon, 10(21), e39770. https://doi.org/10.1016/j.heliyon.2024.e39770
Amien, J. Al, Yoze Rizki, & Mukhlis Ali Rahman Nasution. (2022). Implementasi Adasyn Untuk Imbalance Data Pada Dataset UNSW-NB15 Adasyn Implementation For Data Imbalance on UNSW-NB15 Dataset. Jurnal CoSciTech (Computer Science and Information Technology), 3(3), 242–248. https://doi.org/10.37859/coscitech.v3i3.4339
Araf, I., Idri, A., & Chairi, I. (2024). Cost-sensitive learning for imbalanced medical data: a review. Artificial Intelligence Review, 57(4), 80. https://doi.org/10.1007/s10462-023-10652-8
Bhandary, R., & Ghosh, B. K. (2025). Credit Card Default Prediction: An Empirical Analysis on Predictive Performance Using Statistical and Machine Learning Methods. Journal of Risk and Financial Management, 18(1), 23. https://doi.org/10.3390/jrfm18010023
Calvianto Lim, R. (2026). Comparative Analysis of Decision Tree, Random Forest, and XGBoost for Student Category Prediction. International Journal of Computer Science and Humanitarian AI, 3(1), 9–19. https://doi.org/10.21512/ijcshaijournal.v3i1.14936
Hairani, H., & Priyanto, D. (2023). A New Approach of Hybrid Sampling SMOTE and ENN to the Accuracy of Machine Learning Methods on Unbalanced Diabetes Disease Data. International Journal of Advanced Computer Science and Applications, 14(8). https://doi.org/10.14569/IJACSA.2023.0140864
Han, H., Wang, W.-Y., & Mao, B.-H. (2005). Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning (pp. 878–887). https://doi.org/10.1007/11538059_91
Hancock, J., Khoshgoftaar, T. M., & Johnson, J. M. (2022). Informative Evaluation Metrics for Highly Imbalanced Big Data Classification. 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), 1419–1426. https://doi.org/10.1109/ICMLA55696.2022.00224
Hancock, J. T., Khoshgoftaar, T. M., & Johnson, J. M. (2023). Evaluating classifier performance with highly imbalanced Big Data. Journal of Big Data, 10(1), 42. https://doi.org/10.1186/s40537-023-00724-5
Husain, G., Nasef, D., Jose, R., Mayer, J., Bekbolatova, M., Devine, T., & Toma, M. (2025). SMOTE vs. SMOTEENN: A Study on the Performance of Resampling Algorithms for Addressing Class Imbalance in Regression Models. Algorithms, 18(1), 37. https://doi.org/10.3390/a18010037
Komisarenko, V., & Kull, M. (2025). Cost-sensitive classification with cost uncertainty: do we need surrogate losses? Machine Learning, 114(6), 132. https://doi.org/10.1007/s10994-024-06634-8
Lestari, D. R., Bangun, E. A. S., Gaol, F. L., & Matsuo, T. (2025). Machine Learning-Based Forecasting of Agricultural Commodity Prices Using Ensemble Models. Journal of Human, Earth, and Future, 6(4), 887–899. https://doi.org/10.28991/HEF-2025-06-04-09
Lianata, I. W., Nicholas, K., Nugroho, D., Nathanael, Y., Christopher, N., & Irwansyah, E. (2025). Implementation of Random Forest Algorithm in Handling Imbalanced Data: A Study on Default Models and Hyperparameter Tuning. International Journal of Computer Science and Humanitarian AI, 2(2), 69–74. https://doi.org/10.21512/ijcshaijournal.v2i2.14417
Liany, D., & Oktavia, T. (2024). Comparative Evaluation of Data Mining Classification Techniques for Assistant Recruitment in a Computer Laboratory. Journal of Logistics, Informatics and Service Science, 11(6), 184–197. https://doi.org/10.33168/JLISS.2024.0611
Ling, C. X., & Sheng, V. S. (2008). Cost-Sensitive Learning and the Class Imbalance Problem Motivation and Background. Encyclopedia of Machine Learning.
Mahendra, K., & Oktavia, T. (2024). DATA-DRIVEN INSIGHTS INTO FISHING PATTERNS USING VMS AND MACHINE LARNING. Journal of Theoretical and Applied Information Technology, 102(22). www.jatit.org
Mapfumo, I., & Shongwe, T. (2026). Performance Evaluation of Machine Learning and Deep Learning Models for Credit Risk Prediction. Journal of Risk and Financial Management, 19(3). https://doi.org/10.3390/jrfm19030210
Mauritsius, T., Haris, W., Atmaja, K., Isa, M., Bin Man, M., Oktavia, T., Retnowardhani, A., Komsiyah, S., Atallah, B., & Parikesit, B. (2025). An analytics framework for predicting electric vehicle charging station demand using spatial and interpretive methods. Decision Analytics Journal, 17, 100648. https://doi.org/10.5281/zenodo.1
Mienye, I. D., & Sun, Y. (2021). Performance analysis of cost-sensitive learning methods with application to imbalanced medical data. Informatics in Medicine Unlocked, 25. https://doi.org/10.1016/j.imu.2021.100690
Mishra, S., Mallick, P. K., Jena, L., & Chae, G.-S. (2020). Optimization of Skewed Data Using Sampling-Based Preprocessing Approach. Frontiers in Public Health, 8. https://doi.org/10.3389/fpubh.2020.00274
Pan, T., Zhao, J., Wu, W., & Yang, J. (2020). Learning imbalanced datasets based on SMOTE and Gaussian distribution. Information Sciences, 512. https://doi.org/10.1016/j.ins.2019.10.048
Pes, B., & Lai, G. (2021). Cost-sensitive learning strategies for high-dimensional and imbalanced data: a comparative study. PeerJ Computer Science, 7, e832. https://doi.org/10.7717/peerj-cs.832
Pinheiro, J. M. H., Oliveira, S. V. B. de, Silva, T. H. S., Saraiva, P. A. R., Souza, E. F. de, Godoy, R. V., Ambrosio, L. A., & Becker, M. (2025). The Impact of Feature Scaling in Machine Learning: Effects on Regression and Classification Tasks. IEEE Access, 13. https://doi.org/10.1109/ACCESS.2025.3635541
Riantika, I., Sartono, B., & Anwar Notodiputro, K. (2024). Effectiveness of SMOTE-ENN to Reduce Complexity in Classification Model. Indonesian Journal of Statistics and Its Applications, 8(1). https://doi.org/10.29244/ijsa.v8i1p70-82
Shofiani, P. (2018). Pengaruh Pembiayaan, Non Performing Finance (NPF) dan Dewan Pengawas Syariah Terhadap Manajemen Laba Menggunakan Cadangan Kerugian Penurunan Nilai (CKPN) Perbankan Syariah di Indonesia. CIMAE: Conference on Islamic Management Accounting and Economics, 1.
Toleva, B. (2026). Is There a Preferred Cross‐Validation Type for Train/Test Split in Imbalanced Classification. Applied Computational Intelligence and Soft Computing, 2026(1). https://doi.org/10.1155/acis/6832228
Xiao, J., Wang, Y., Chen, J., Xie, L., & Huang, J. (2021). Impact of resampling methods and classification models on the imbalanced credit scoring problems. Information Sciences, 569, 508–526. https://doi.org/10.1016/j.ins.2021.05.029
Xie, W., & Huang, X. (2025). Oversampling Algorithm Based on Improved K-Means and Gaussian Distribution. Information, 17(1), 28. https://doi.org/10.3390/info17010028
Xie, Y., Qiu, M., Zhang, H., Peng, L., & Chen, Z. (2022). Gaussian Distribution Based Oversampling for Imbalanced Data Classification. IEEE Transactions on Knowledge and Data Engineering, 34(2), 667–679. https://doi.org/10.1109/TKDE.2020.2985965
Yang, C., Dong, Y., Lu, J., & Peng, Z. (2022). Solving Imbalanced Data in Credit Risk Prediction: A Comparison of Resampling Strategies for Different Machine Learning Classification Algorithms, Taking Threshold Tuning into Account. 2022 5th International Conference on Machine Learning and Machine Intelligence, 30–40. https://doi.org/10.1145/3568199.3568204
Zhang, X., Zhang, T., Hou, L., Liu, X., Guo, Z., Tian, Y., & Liu, Y. (2025). Data-Driven Loan Default Prediction: A Machine Learning Approach for Enhancing Business Process Management. Systems, 13(7), 581. https://doi.org/10.3390/systems13070581
Zhao, Z., Cui, T., Ding, S., Li, J., & Bellotti, A. G. (2024). Resampling Techniques Study on Class Imbalance Problem in Credit Risk Prediction. Mathematics, 12(5), 701. https://doi.org/10.3390/math12050701
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