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All Journal Ekonomikawan : Jurnal Ilmu Ekonomi dan Studi Pembangunan Jurnal Akuntansi dan Pajak Li Falah: Jurnal Studi Ekonomi dan Bisnis Islam JCRS (Journal of Community Research and Service) Jurnal Abdi Ilmu Jurnal Pendidikan dan Konseling Computer Science and Information Technologies Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal AKMAMI (Akuntansi Manajemen Ekonomi) INVEST : Jurnal Inovasi Bisnis dan Akuntansi IJORER : International Journal of Recent Educational Research Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Journal of Research in Social Science and Humanities Proceeding International Seminar of Islamic Studies Jurnal Bina Bangsa Ekonomika EKONOMIKA45 Proceeding of The International Conference on Economics and Business Proceedings of The International Conference on Business and Economics Journal of Economics and Social Sciences Jurnal Manajemen Dan Akuntansi Medan Jurnal Pengabdian West Science Journal of Management, Economic, and Accounting Jurnal Ilmiah Ekonomi dan Manajemen International Journal of Sustainable Applied Sciences (IJSAS) Journal of Intelligent Systems and Information Technology International Journal of Economics, Business and Innovation Research International Journal of Management, Economic and Accounting Green Economics: International Journal of Islamic and Economic Education Proceedings of The International Conference on Computer Science, Engineering, Social Sciences, and Multidisciplinary Studies Global Economics: International Journal of Economic, Social and Development Sciences
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Journal : Journal of Intelligent Systems and Information Technology

Implementation of Machine Learning Algorithm for Credit Scoring Prediction in Islamic Microfinance Siregar, Kiki Hardiansyah; Ruslan, Dede; Faried, Annisa Ilmi; Sembiring, Rahmad
Journal of Intelligent Systems and Information Technology Vol. 2 No. 2 (2025): July
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v2i2.156

Abstract

Islamic microfinance institutions face complex challenges in data management and customer behavior prediction in the digital era. This study aims to optimize the Gradient Boosting algorithm with pruning techniques to predict customer collectibility. The analysis was conducted on data from 57 customers with 7 attributes from 2022 to 2024. The research methodology includes four stages: data collection, pre-processing, modeling, and evaluation. Pre-processing involves handling missing data, normalization, encoding, and feature selection. Modeling using XGBoost with and without pruning, followed by evaluation using accuracy, precision, recall, F1-score, confusion matrix, and ROC curve metrics. The results show an increase in model performance with pruning: accuracy increased by 0.70%, precision 0.60%, recall 0.80%, and F1-score 0.70%. This technique is effective in reducing overfitting and increasing model generalization. This research provides significant contribution in developing more accurate credit scoring system for Islamic microfinance institutions, improving credit risk management and customer service in Islamic microfinance sector. The findings help Islamic microfinance institutions optimize credit decision-making process and reduce risk in the digital era.
Utilizing of Big Data and Machine Learning to Predict the Impact of Sharia Monetary Policy on Economic Stability Siregar, Kiki Hardiansyah; Ruslan, Dede; Faried, Annisa Ilmi; Sembiring, Rahmad; Andriani, Maya; Swantika, Ika
Journal of Intelligent Systems and Information Technology Vol. 3 No. 1 (2026): January
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v3i1.243

Abstract

This study aims to investigate the use of big data and machine learning techniques to predict the effects of Sharia monetary policy on economic stability. Motivated by the underexplored nature of Sharia-compliant financial forecasting, the research applies various machine learning models including Random Forest, XGBoost, Support Vector Regression (SVR), Linear Regression, and Long Short-Term Memory (LSTM) networks to long-term stock and monetary trend predictions within the Islamic finance context. Utilizing a dataset of Indonesian Sharia stocks and financial indicators, and employing SHAP analysis for model interpretability, the study evaluates model performance based on metrics like MAPE and R². Results reveal that SVR outperforms other algorithms, providing robust and interpretable predictions that capture the influence of key financial indicators such as moving averages and Sharia-compatible BI Rate transmissions. The findings have practical implications for enhancing financial inclusion and promoting risk-efficient, interest-free profit-sharing models, with national Sharia financial assets projected to exceed Rp10.5 quadrillion by 2026. The study fills gaps in academic literature on Islamic monetary forecasting and sets a foundation for future integration of hybrid AI models for real-time policy evaluation, supporting the digital transformation of Sharia banking aligned with maqasid syariah principles