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All Journal Journal of Economic, Bussines and Accounting (COSTING) Community Engagement and Emergence Journal (CEEJ) Jurnal Ekonomi Syariah dan Bisnis Journal of Artificial Intelligence and Digital Business Jurnal Pengabdian West Science Jurnal Pengabdian Masyarakat Bangsa Journal of Intelligent Systems and Information Technology Joong-Ki
Siregar, Kiki Hardiansyah
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences

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Journal : Journal of Intelligent Systems and Information Technology

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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
Co-Authors , Rahmad Sembiring Andriani, Maya Collin, Evelyn Laurencia Dede Ruslan Efendi, Bakhtiar Faried, Annisa Ilmi Farried, Annisa Ilmi Ferry Fong Ginting, Yara Ainy Br Herlin Munthe Lahagu, Dasar Iman Mahyarni - Marbun, Novita Royana Rafida Khairani Ritonga, Nazamuddin Selvia, Dinda Sheryn, Sheryn Swantika, Ika Vientiany, Dini Yonggi, Stepano Giuseppe