Zakat Recipient Recommendation System Based on Machine Learning Approach
DOI:
https://doi.org/10.31313/nacmpw09Keywords:
Zakat, Mustahik, Random ForestAbstract
Background: Zakat is an Islamic obligation aimed at reducing economic inequality and providing assistance to those in need. However, the distribution of zakat is often complex and requires an efficient and objective decision-making process. The development of information technology, particularly machine learning, offers opportunities to improve the effectiveness and accuracy of zakat recipient selection.
Purpose: This study aims to examine the application of a machine learning-based recommendation system in determining zakat recipients and to highlight its potential in supporting objective, accurate, and efficient zakat distribution.
Methods: This study employs a literature review approach by analyzing previous studies related to zakat distribution, decision support systems, data mining, and machine learning methods, particularly recommendation systems and the Random Forest algorithm.
Results: The findings indicate that machine learning-based approaches can assist in identifying eligible zakat recipients more objectively and accurately. The use of recommendation systems and Random Forest algorithms reduces subjectivity in decision-making, accelerates the selection process, and minimizes errors in zakat distribution. In addition, system-based approaches can improve the overall efficiency of zakat management.
Conclusions: The implementation of machine learning in zakat recipient recommendation systems has significant potential to improve the quality of zakat distribution. Nevertheless, the application of such technology should remain aligned with the religious and social values of zakat to ensure that its spirit and objectives are preserved.
Research Contribution: This study contributes to the development of technology-based zakat management by providing a conceptual understanding of how machine learning can be integrated into zakat recipient selection systems to support fairer, more effective, and more accountable distribution.
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