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All Journal Journal of Intelligent Systems and Information Technology Journal Of Management, Economics, And Accounting Research
Annisa Ilmi Faried
Universitas Pembangunan Panca Budi Medan
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Social Sciences

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Economic Valuation of Agricultural Productivity Enhancement through Digital-Based Resource Conservation Annisa Ilmi Faried; Vina Arnita; Nisa Ulzannah
Journal of Management, Economics, and Accounting Research Vol. 1 No. 1 (2025): November 2025
Publisher : CV. Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/jomear.v1i1.30

Abstract

The agricultural sector faces increasing pressure to enhance productivity while maintaining the sustainability of natural resources. Digital technologies offer significant opportunities to optimize resource use and support conservation-oriented farming systems, yet their economic value is often insufficiently quantified. This study examines the economic valuation of agricultural productivity enhancement through digital-based resource conservation. Using a conceptual–analytical approach supported by evidence from recent empirical studies, this paper integrates economic valuation frameworks with digital agriculture practices, including precision farming, data-driven resource management, and decision-support technologies. The analysis highlights how digital interventions generate direct financial benefits through productivity gains and cost efficiency, as well as indirect values related to ecosystem services, option values for future innovation, and social welfare improvements. The findings indicate that digital-based resource conservation contributes not only to higher agricultural output but also to long-term sustainability by reducing environmental degradation and enhancing adaptive capacity to climate change. This study provides a comprehensive valuation perspective that supports policy formulation and strategic investment in digital agriculture systems. The results underscore the importance of incorporating economic valuation into agricultural system planning to ensure balanced outcomes between productivity growth and resource conservation
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 Dede Ruslan Ika Swantika Kiki Hardiansyah Siregar Maya Andriani Nisa Ulzannah Rahmad Sembiring Vina Arnita