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All Journal Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi International Transactions on Artificial Intelligence (ITALIC)
Rajendra Kumar
Sharda University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Library & Information Science Other

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The Role of Artificial Intelligence in Strengthening Sharia-Based Educational Governance Rajendra Kumar; Abdul Wahab Abdul Rahman
Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Vol 5 No 1 (2026): September
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/mentari.v5i1.1226

Abstract

The rapid development of Artificial Intelligence (AI) has created new opportunities for improving educational governance through more efficient decision-making, data management, transparency, and accountability across increasingly complex educational systems worldwide. However, its implementation in education requires alignment with ethical values and Islamic principles to ensure that technological advancement does not undermine fairness, privacy, responsibility, and human-centered decision-making. This study aims to examine the role of AI in strengthening Sharia-based educational governance and identify the opportunities, benefits, and challenges associated with its implementation. The study employs a Systematic Literature Review (SLR) by analyzing relevant academic publications that discuss AI applications, educational governance, Islamic ethics, and Sharia-based principles. The literature was reviewed systematically to identify recurring themes, relationships, and research gaps concerning the integration of AI into educational governance. The results indicate that AI can strengthen Sharia-based educational governance by supporting data-driven decision-making, improving administrative efficiency, enhancing transparency, and facilitating more accountable educational services. Nevertheless, the findings also reveal challenges related to data privacy, algorithmic bias, technological dependency, ethical responsibility, and the need for human oversight. In conclusion, AI has significant potential to support Sharia-based educational governance when its implementation is guided by Islamic ethical principles, transparency, accountability, justice, and responsible data management. This study contributes a conceptual understanding of how AI can be integrated into educational governance while maintaining the fundamental values of Sharia.
Machine Learning-Based Prediction of Electric Vehicle Charging Demand for Sustainable Transportation Nyree Ani; Nuke Puji Lestari Santoso; Rajendra Kumar
International Transactions on Artificial Intelligence Vol. 5 No. 1 (2026): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v5i1.1245

Abstract

The increasing adoption of Electric Vehicles (EV) presents challenges in managing uncertain charging demand, optimizing infrastructure utilization, and maintaining energy system stability. This study aims to develop and evaluate a machine learning-based prediction framework for estimating EV charging energy demand and identifying the most effective predictive algorithm to support sustainable transportation planning. The proposed framework employs historical EV charging observations and contextual variables, including charging duration, charging time, day of the week, ambient temperature, charging frequency, and historical energy consumption. Three supervised machine learning algorithms, namely Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost), are comparatively evaluated. Predictive performance is assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The reported experimental findings indicate that XGBoost achieves the highest predictive accuracy, with an RMSE of 2.84 kWh, MAE of 1.97 kWh, MAPE of 6.21%, and R² of 0.941. However, the comparative performance metrics for RF and SVR must be reported in the main Results section to substantiate the claimed superiority of XGBoost. The proposed framework demonstrates the potential of machine learning to identify charging demand patterns using historical charging, temporal, and environmental information. Subject to further experimental verification, the findings may support data-driven charging infrastructure management, electricity resource allocation, and sustainable transportation planning.
Co-Authors Abdul Wahab Abdul Rahman Nuke Puji Lestari Santoso Nyree Ani