Zakat Management System with Allocation Prediction Using Case-Based Reasoning
Abstract
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.
References
[2] N. H. S. Mohamed, N. A. Mastuki, S. N. S. Yusuf, and M. Zakaria, “Management control system in ASNAF entrepreneurship development program by Lembaga Zakat Selangor,” J. Pengur., vol. 53, no. 2018, pp. 13–22, 2018.
[3] M. D. Rahmatya and M. F. Wicaksono, “Model of receipt and distribution of zakat funds information system,” IOP Conf. Ser. Mater. Sci. Eng., vol. 407, no. 1, 2018.
[4] F. A. A. Nazri, R. A. Rahman, and N. Omar, “Zakat and Poverty Alleviation : Roles of Zakat Institutions in Malaysia,” Int. J. Arts Commer., vol. 1, no. 7, pp. 61–72, 2012.
[5] K. P. Tripathi, “A Review on Knowledge-based Expert System : Concept and Architecture,” Artif. Intell. Tech. - Nov. Approaches Pract. Appl., vol. 4, no. 4, pp. 19–23, 2011.
[6] P. S. Sajja and R. Akerkar, “Knowledge-Based Systems for Development,” Adv. Knowl. Based Syst. Model. Appl. Res., vol. 1, pp. 1–11, 2010.
[7] M. B. Bentaiba-Lagrid, L. Bouzar-Benlabiod, S. H. Rubin, T. Bouabana-Tebibel, and M. R. Hanini, “A case-based reasoning system for supervised classification problems in the medical field,” Expert Syst. Appl., vol. 150, p. 113335, 2020.
[8] E. Mendes, N. Mosley, and I. Watson, “A comparison of case-based reasoning approaches,” Proc. 11th Int. Conf. World Wide Web, WWW ’02, pp. 272–280, 2002.
[9] H. Hadj-Mabrouk, “Application of Case-Based Reasoning to the safety assessment of critical software used in rail transport,” Saf. Sci., vol. 131, no. May, p. 104928, 2020.
[10] N. Khairudin, S. A. Mohd Noah, A. Azizan, and A. B. Jelani, “Case-based diabetic dietary plan using memory organization packets,” Proc. - 2012 Int. Conf. Inf. Retr. Knowl. Manag. CAMP’12, pp. 91–94, 2012.
[11] S. Dutta and P. P. Bonissone, “Integrating case- and rule-based reasoning,” Int. J. Approx. Reason., vol. 8, no. 3, pp. 163–203, 1993.
[12] S. Deris, S. Omatu, and H. Ohta, “Timetable planning using the constraint-based reasoning,” Comput. Oper. Res., vol. 27, no. 9, pp. 819–840, 2000.
[13] P. Berka, “NEST: A Compositional Approach to Rule-Based and Case-Based Reasoning,” Adv. Artif. Intell., vol. 2011, pp. 1–15, 2011.
[14] C. G. Von Wangenheim, “Case-Based Reasoning – A Short Introduction,” Cycle, pp. 1–7, 2000.
[15] A. Agnar and E. Plaza, “Case-Based reasoning: Foundational issues, methodological variations, and system approaches,” AI Commun., vol. 7, no. 1, pp. 39–59, 1994.
[16] J. L. Kolodner, “An Introduction to Case-Based Reasoning,” Artif. Intell. Rev., vol. 6, pp. 3–34, 1992.
[17] G. Finnie and Z. Sun, “Similarity and metrics in case-based reasoning,” Int. J. Intell. Syst., vol. 17, no. 3, p. 273, 2002.
[18] D. K. Choubey and S. Paul, “GA_SVM: A classification system for diagnosis of diabetes,” Handb. Res. Soft Comput. Nature-Inspired Algorithms, pp. 359–397, 2017.
