<![CDATA[The increasing amount of donation data requires a data analysis process to identify information patterns that can be utilized to support decision-making. The problem in donation data management at Yayasan Nur Hidayah is the absence of data utilization to identify donor behavior patterns based on donation time, donation amount, and donation location. Therefore, this study aims to implement the Apriori algorithm to discover relationships among donation data through the association rule process. The research method used is Knowledge Discovery in Databases (KDD), which consists of data selection, preprocessing, transformation, data mining, and evaluation stages. The data used consisted of 50 donation transactions with attributes including donation date, donation amount, and donation location. The analysis process was carried out using the Apriori algorithm by measuring support and confidence values to generate association rules. The results showed that the Apriori algorithm successfully identified relationship patterns among donation data. The association rule with the highest confidence value was found in the pattern “TD3 → Mid-Month” with a confidence value of 84.61%, indicating that donation transactions at the TD3 donation location tended to occur in the middle of the month. The results of this study can be utilized as a basis for decision support in determining more effective and data-driven donation fundraising strategies.]]>
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