MITIGATING POVERTY: THE CLUSTERING OF POTENTIAL ZAKAT IN INDONESIA

The objective of this study was to examine the fuzzy c-means clustering (FCM) method to establish the optimum cluster accuracy of zakat potential in Indonesia. A spatial mapping approach is also suggested and can be considered as the first step in knowing the distribution of zakat potential in Indonesia. Furthermore, strategies that can be implemented are formulated to increase zakat collection in Indonesia. Potential zakat data from the National Amil Zakat Agency (Baznas) in 2020 consisting of bank deposits, salaries, agricultural products, plantation products, and staple foods. Each province in Indonesia is used as the proposed variable. In this paper, firstly collecting data on indicators of potential zakat. Second, the FCM clustering algorithm. Third, the results of the FCM grouping are visualized in the form of a mapping. This novel mapping study with FCM was applied in order to analyze clustering accuracy. The FCM results confirm 2 optimum clusters for zakat potential in Indonesia where cluster 2 has more members than cluster 1. Besides, the second cluster only has one variable that has a high value, namely agricultural products, while the rest is in the first cluster. This indicates that the first cluster has a higher potential for zakat. The application of fuzzy c-means (FCM) to obtain the optimum cluster on zakat potential to produce a mapping of zakat potential is a novelty in the field of Islamic economic studies. Finally, the results of the analysis with this approach provide optimum results to strengthen the zakat collection strategy in Indonesia.