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Journal of Intelligent Systems and Information Technology
Published by Apik Cahaya Ilmu
ISSN : -     EISSN : 30465001     DOI : https://doi.org/10.61971/jisit
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Journal of Intelligent Systems and Information Technology (JISIT) focuses on providing scientific articles related to Intelligent Systems and Information Technology, which are developed by publishing articles, research reports and reviews. Journal of Intelligent Systems and Information Technology (JISIT) accepts scientific articles in the field of research: Artificial Intelligence, Data Mining, Text Mining, Web Mining, Machine Learning, Deep Learning, Natural Language Processing (NLP), Social Network Analysis, Expert system, Decision Support System, Computer Network Security related AI, Image processing, Computer Vision, Big Data, and related fields
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 1 Documents
 1 
Search results for , issue "Vol. 3 No. 1 (2026): January" : 1 Documents clear
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

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