Citation

Mohd Zebaral Hoque, Jesmeen and Abd Aziz, Azlan and Tawsif Khan, Chy. Mohammed and Othman, Khair Razlan and Kamaruddin, Mohd Nazeri and Amir Hamzah, Nur Asyiqin (2025) Enhancing Zakat Collection Forecasting Using CNNs to Improve Planning and Resource Allocation. In: 2025 Multimedia University Engineering Conference, MECON 2025, 21 July 2025 - 23 July 2025, Cyberjaya, Malaysia.

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Abstract

Zakat, one of the fundamental pillars of Islam, plays a vital role in promoting social justice through wealth redistribution. However, many Zakat institutions face persistent challenges such as poor planning, under-collection, and inefficient distribution, often due to a lack of data-driven forecasting. This paper explores the application of Convolutional Neural Networks (CNNs) to predict future zakat collections using historical financial and categorical data. By training models on real annual zakat data from 2014 to 2023, further potential collections up to 2026 forecasting were executed. Performance evaluation shows that the Deeper CNN model achieved exceptional predictive accuracy, with mean absolute percentage errors (MAPE) as low as 0.03%-0.07% across different datasets. These results demonstrate the model's ability to capture complex temporal patterns effectively. Importantly, this forecasting approach offers actionable insights for Zakat institutes, enhancing transparency, improving resource planning, and increasing stakeholder trust. This research highlights how modern machine learning tools can be thoughtfully integrated into traditional Islamic finance systems to strengthen impact and accountability.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Zakat forecasting, Convolutional Neural Networks (CNN)
Subjects:	B Philosophy. Psychology. Religion > BP Islam. Bahaism. Theosophy, etc Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	18 Mar 2026 07:57
Last Modified:	19 Mar 2026 00:28
URII:	http://shdl.mmu.edu.my/id/eprint/15565

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