Decision Support System for Zakat Asnaf Selection among Uitm Melaka Students Using Artificial Neural Networks

Ismadi Md Badarudin, Ameera Iman Hassan, Suzana Ahmad, Yuzi Mahmud, Khairunnisa Abd Samad and Noor Afni Deraman
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Ismadi Md Badarudin: Fakulti Sains Komputer dan Matematik UiTM
Ameera Iman Hassan: Cloud Mile Sdn. Bhd,
Suzana Ahmad: Cloud Mile Sdn. Bhd,
Yuzi Mahmud: Cloud Mile Sdn. Bhd,
Khairunnisa Abd Samad: Fakulti Pengurusan dan Perniagaan UiTM
Noor Afni Deraman: Fakulti Sains Komputer dan Matematik UiTM

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 23, 179-187

Abstract: This study developed a Decision Support System for Zakat Asnaf Selection (DSSZAS) to address inefficiencies in the manual distribution of zakat among students at Universiti Teknologi MARA (UiTM) Cawangan Melaka. The current process faces challenges in accurately identifying eligible asnaf and distributing promptly. Therefore, to solve this, the DSSZAS leverages Artificial Neural Networks (ANN) to automate the classification of students into asnaf categories (faqr, miskin, and fisabilillah) based on socioeconomic data. The system was designed and trained with historical data using the Waterfall methodology. A comparison method was deployed between the generated result and human decision to test the result reliability. It achieves an accuracy rate of 1.0% with a minimized Mean Squared Error (MSE) of 0.06. The system significantly reduces human bias and enhances efficiency through automated decision-making and email notifications that inform students of their application status., DSSZAS strengthens the fairness and reliability of zakat distribution by providing a transparent and data-driven approach.

Date: 2025
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