<![CDATA[This study aims to explore and uncover hidden patterns in charitable donation transactions (sadaqah and waqf) using the Frequent Pattern Growth (FP-Growth) algorithm as a method of association rule mining. The research employs an experimental design conducted on the Google Colaboratory platform using Python version 3.11.3. The dataset used is the global donation data from Eid Charity, available on the Kaggle platform, comprising 45,842 rows and 33 attributes. The experimental results yielded six association patterns, all of which have the consequence of "mosques," indicating that various forms of donations consistently culminate in fund allocations to mosques. The most significant pattern was found in the combination of “Ensuring preachers and imams are conditional” and “Well Drilling,” which produced the highest metric values in confidence (0.896), lift (3.39), conviction (7.08), and Zhang’s metric (0.714). These findings suggest that mosques serve as the central hub of philanthropic activity within the dataset. The discovered patterns provide deep insights into donor behavior tendencies and can serve as a foundation for more targeted and effective strategies in managing and developing future donation programs.]]>
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