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Hanif Journal of Information Systems
Published by Ilmu Bersama Center
ISSN : -     EISSN : 30252342     DOI : https://doi.org/10.56211/hanif
Core Subject : Science,
Computer Science & IT Library & Information Science
Hanif journal of Information Systems aims to provide scientific literatures specifically on studies of applied research in information systems (IS)/information technology (IT) and public review of the development of theory, method and applied sciences related to the subject. Hanif Journal of Information Systems accepts manuscripts on the topics: E-Business/E-commerce E-Government E-Health E-learning Human-Computer Interaction Information Assurance & Intelligent Information Security & Risk Management IS/IT Operations Management IS/IT Organization & Human Resource Management IS/IT Strategic Planning IT Governance IT Investment Analysis IT Project Management Web Science Social Media in Business Multimedia Application Big Data Research New Technology Acceptance and Diffusion Green Information Systems Innovation Management/Technopreneurship Data Science And other topics relevant to Information Systems.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 41 Documents
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Predicting Non-Performing Loan Levels Using XGBoost and Explainable Data Mining Pastima Simanjuntak; Rika Harman
Hanif Journal of Information Systems Vol. 3 No. 2 (2026): February Edition
Publisher : Ilmu Bersama Center

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Abstract

Non-Performing Loans (NPLs) represent one of the most critical indicators in assessing the stability and financial health of banking institutions. An increasing NPL ratio may negatively affect profitability, liquidity, and overall banking performance, making accurate prediction models essential for effective credit risk management. This study proposes a predictive framework for estimating Non-Performing Loan levels using the Extreme Gradient Boosting (XGBoost) algorithm combined with Explainable Data Mining techniques. The dataset consists of historical banking and financial indicators that influence loan repayment behavior. Data preprocessing stages include data cleaning, feature selection, normalization, and handling of missing values to improve model performance. The XGBoost model was employed due to its ability to handle complex nonlinear relationships and high-dimensional data efficiently. To enhance model transparency and interpretability, Explainable Data Mining techniques based on SHapley Additive exPlanations (SHAP) were applied to identify the contribution and importance of individual features affecting NPL predictions. Experimental results demonstrate that the proposed XGBoost model achieves high predictive performance, outperforming conventional machine learning approaches in terms of accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). Furthermore, the explainability analysis reveals the most influential factors contributing to NPL levels, providing valuable insights for financial institutions and policymakers in developing proactive risk mitigation strategies. The findings indicate that integrating XGBoost with Explainable Data Mining not only improves prediction accuracy but also enhances the interpretability and trustworthiness of credit risk assessment models. This approach can support data-driven decision-making processes and strengthen the sustainability of banking operations in increasingly complex financial environments.

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