Anjheli, MaenaSukamto2024-08-132024-08-132023-12PerpustakaanElfitrahttps://repository.unri.ac.id/handle/123456789/11413Pagaruyung Diesel store in Duri City has experienced Growth in both customers and product variety. The issue at hand is an imbalance in product inventory. Therefore, decisions need to be made based on the highest sales to efficiently manage inventory and enhance customer service. In this regard, transaction sales data is utilized to identify customer purchasing behavior. Data mining technology serves as a valuable tool in inventory information identification at Pagaruyung Diesel store. This technology employs pattern matching strategies and algorithms to uncover relationships within the data. One of the approaches used is the FP-Growth Algorithm, which allows the identification of common Item sets within the data. In this study, the FP-Growth Algorithm is applied to analyze purchasing patterns. The aim of this research is to implement the FP-Growth Algorithm method to analyze purchasing patterns at Pagaruyung Diesel store. The results are expected to assist in inventory management and decision-making. The research findings indicate that by using sales data for automotive spare parts from January to June 2022, the FP-Growth Algorithm method produces 5 association Rules with a Minimum Support of 1% and a Minimum Confidence of 50%. These results demonstrate that the FP-Growth Algorithm can be effectively applied to analyze purchasing patterns at Pagaruyung Diesel store.enData MiningFP-Growth AlgorithmPurchase PatternsArticle