<![CDATA[Spare part inventory management is an important aspect of preventive maintenance activities. This study aims to analyze the performance comparison between the Apriori and FP-Growth algorithms in identifying spare part purchasing patterns for preventive maintenance activities. The main problem in spare part management is the lack of optimal inventory planning, which can lead to overstock or stock shortages. The method used in this study is Association Rule Mining with two algorithms, namely Apriori and FP-Growth, applied to spare part purchasing transaction data. The analysis process was conducted through data preprocessing, frequent itemset generation, and association rule formation using minimum support and confidence parameters. The results indicate that the FP-Growth algorithm performs more efficiently than Apriori in terms of computation time and the ability to handle large datasets. Meanwhile, the Apriori algorithm is easier to implement and understand. The resulting association patterns can be used as a basis for decision-making in more effective and efficient spare part inventory management. Therefore, this study is expected to contribute to improving data-driven preventive maintenance strategies.]]>
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