Zakat Management System with Allocation Prediction Using Case-Based Reasoning

  • Nurkhairizan Khairudin Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perak Branch Tapah Campus, Perak, Malaysia
  • Nurul Ain Azlan Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perak Branch Tapah Campus, Perak, Malaysia
  • Azilawati Azizan Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perak Branch Tapah Campus, Perak, Malaysia
  • Ahmad Bakhtiar Jelani Academy of Contemporary Islamic Studies, Universiti Teknologi MARA, Perak Branch Tapah Campus, Perak, Malaysia


Zakat has become one of the vital opportunity to be given to the poor and needy. However, there are problems faced by the institution of zakat with the inefficiency and inaccurate issue, especially in the zakat allocation and distribution aspects. Moreover, the zakat allocation and distribution process is time consuming due to the variety of the criteria to be considered, especially when it involves an educational institution. Since the problem usually originates from the organization of zakat itself, it is essential to minimize the difficulties so that zakat can be distributed in a proper way to the qualified person with a suitable allocation. Therefore, the purpose of this project is to develop a web-based Zakat Management and Allocation Prediction System using Case-based Reasoning(CBR) technique. The proposed method consists of two components: (1) Web-based zakat management system which aims to properly manage all related data of the zakat applicant, and (2) Zakat allocation module using CBR to suggests the allocation amount of zakat by finding the similarities between the previous cases and the new cases. For the prediction purposes, the significant main features are identified and suitable weightage is assigned to be able the CBR engine to produce a suggestion. Experimental results using real data collected from UiTM(Perak) Tapah Campus show that our proposed model achieves a significant improvement in the efficiency of managing and allocating the amount of zakat.


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How to Cite
KHAIRUDIN, Nurkhairizan et al. Zakat Management System with Allocation Prediction Using Case-Based Reasoning. Mathematical Sciences and Informatics Journal, [S.l.], v. 1, n. 2, p. 22-33, nov. 2020. ISSN 2735-0703. Available at: <>. Date accessed: 19 june 2024. doi:

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