Enhancing the zakat financial assistance model using Polytomous logistic regression: a case study at UiTM Perlis branch during the COVID-19 pandemic

Zakat, Sedekah, and W akaf Unit (ZAWAF) of UiTM Perlis Branch has modified its zakat application process to an efficient online system during COVID-19. However, the unit faces challenges in accurately ensuring appropriate zakat assistance is provided. Therefore, this study conducted Polytomous Logistic Regression to determine the appropriated amount for eligible student who received zakat by using SPSS software. The research methodology involves collecting zakat application data from UiTM campuses in Arau over a period of four semesters, from March 2020 to October 2021. In addition to the family size, gender, state, faculty, programme, semester, CGPA, student status, and other factors, the study expands the analysis by including variables of household income groups (B40, M40, T20). The data splitting for test 2 (2semesters training set and 2 semesters test set) was chosen as the best fit model considering a few statistical analyses. The result shows Mother's income, Household size, Faculty, Semester, Zakat receiver, and Head's family income were statistically significant with an overall percentage correct accuracy of71.2%.