NEW ZAKAT DISTRIBUTION MODEL USING SUPERVISED MACHINE LEARNING MODEL: A CASE STUDY IN UITM CAWANGAN PERLIS. - Azlan Abdul Aziz; Nor Azriani Mohamad Nor; Wan Nurshazelin Wan Shahidan; Siti Nor Nadrah Muhamad; Nur Syuhada Muhammat Pazil, (INTERNATIONAL JOURNAL OF ENTREPRENEURSHIP AND MANAGEMENT PRACTISES (IJEMP), 2024)
The new coronavirus (COVID-19) spread in early 2020 and affected health, economy, industry and education worldwide. To mitigate the effects of COVID-19, the Malaysian government imposed the Movement Control Order (MCO). Borders were closed, commercial activity ceased, and all academic institutions were closed. Students need to undergo online home learning, which causes difficulties, especially students from B40 groups. Concerned with the problems faced by UiTM Cawangan Perlis students, especially those from low-income families, the Unit Zakat, Sedekah & Wakaf (ZAWAF) has offered financial assistance in the form of zakat. However, the zakat distribution process is time-consuming. Therefore, this study aims to build a new zakat distribution model based on a Supervised Machine Learning model. The results show that parents’ income and household size significantly contributed to the success of students receiving zakat assistance. The results of this study suggest that developing a creative approach could help to streamline routine processes. Hence, the department of zakat can resolve the administrative and financial inefficiencies that have rendered zakat institutions ineffective.