<![CDATA[Donation data management is a complex challenge for amil zakat institutions in designing an effective marketing strategy for fundraising programs. In this study, the k-medoids algorithm was used to cluster the donation data with the aim of identifying patterns and characteristics of donors. The k-medoids algorithm was chosen because of its ability to handle unusual data and non-numeric attributes. Through clustering analysis, this study classifies donors based on attributes such as the number of donations, the frequency of donations, and the time interval for donating. And in determining the number of clusters in this study using the silhouette method to measure the quality of the resulting clustering. And getting the most optimal number of clusters is k = 3 with a silhouette score of 0.598782. The results of the study found that groups of donors had similar characteristics, such as donors who made high donations with regular frequency and donors who focused on donations for specific purposes. These findings can be used by charitable organizations in developing more effective fund management, marketing and targeting strategies]]>
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