<![CDATA[Abstract — This study aims to analyze drug transaction patterns using association rule mining on pharmacy sales data at Prima Medika Pharmacy, Metro City. The dataset consists of 663 transactions collected from January to February 2026. The research process includes data selection, data cleaning, and data transformation as preprocessing steps. The implementation was carried out using RapidMiner with the FP-Growth operator for generating frequent itemsets and the Create Association Rules operator for producing association rules. The minimum support and minimum confidence thresholds were set to 0.005 and 0.3, respectively. The results show various association patterns between drugs based on support, confidence, and lift values, where some rules exhibit high confidence but are supported by a small number of transactions, requiring careful interpretation. This study provides insights into purchasing patterns that can support inventory management and pharmacy service planning. Key words — Apriori algorithm, association rule, data mining, medicine sales transaction, purchase pattern.]]>
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