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p-Index From 2021 - 2026
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All Journal GANDRUNG: Jurnal Pengabdian Kepada Masyarakat Journal of Intelligent Systems and Information Technology
Maya Andriani
Sekolah Tinggi Ilmu Ekonomi Profesional Indonesia Medan
Computer Science & IT Engineering Physics Other

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Socialization of UMKM Merchandise Inventory Management During the Covid 19 Pandemic in Desa Suka Makmur Harkim Simamora; Rejekia Vaizal Simanungkalit; Maya Andriani; Bambang Sugiharto
GANDRUNG: Jurnal Pengabdian Kepada Masyarakat Vol. 2 No. 2 (2021): GANDRUNG: Jurnal Pengabdian Kepada Masyarakat
Publisher : Fakultas Olahraga dan Kesehatan, Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/gandrung.v2i2.1383

Abstract

The Covid pandemic has had many impacts on people's lives, one of which is the economy. The spread of the virus that requires human activities to be carried out by social distancing (maintaining social distance) and even implementing lockdown measures that have an impact on slowing economic activity (demand and supply). UMKM are one of the sectors affected by this pandemic where there is not only a decline in income, but another impact that is felt by business actors is inventory that accumulates. The implementation of this service activity has provided an understanding to UMKM actors about the importance of inventory management for UMKMs and increased the participants' ability to manage UMKM finances so that they can survive during this pandemic.
Utilizing of Big Data and Machine Learning to Predict the Impact of Sharia Monetary Policy on Economic Stability Kiki Hardiansyah Siregar; Dede Ruslan; Annisa Ilmi Faried; Rahmad Sembiring; Maya Andriani; Ika Swantika
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 Annisa Ilmi Faried Bambang Sugiharto Dede Ruslan Harkim Simamora Ika Swantika Kiki Hardiansyah Siregar Rahmad Sembiring Rejekia Vaizal Simanungkalit