<![CDATA[Drug inventory control requires information on patterns of drug co-occurrence in healthcare service transactions. This study applies the FP-Growth algorithm to identify frequent itemsets and association rules in drug dispensing data to support inventory control at Estomihi Hospital. The research stages include data preprocessing, FP-Tree construction, frequent itemset discovery, and rule evaluation using support, confidence, and lift ratio. Testing using 360 simulated transactions with a minimum support of 5% and a minimum confidence of 60% generated 38 frequent itemsets and 46 association rules with lift values greater than 1. The results indicate that increasing the minimum support and confidence thresholds reduces the number of generated patterns. FP-Growth has the potential to help identify groups of drugs that require joint monitoring. However, the simulation-based results do not reflect the hospital's actual conditions; therefore, their practical application requires validation using official transaction data.]]>
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