<![CDATA[Household furniture sales at Prabot Kukuh Store in Aceh Tenggara exhibit significant monthly fluctuations, while the manual transaction analysis conducted by the store makes it difficult to accurately identify customer purchasing patterns. This limitation affects strategic decision-making related to stock management, product placement, and promotional planning. Therefore, this study aims to analyze sales patterns using data mining techniques, specifically the Frequent Pattern Growth (FP-Growth) algorithm, to provide strategic recommendations for the store. The dataset consists of 1,830 sales transactions collected through observation, interviews, and documentation from June to December 2024. The research stages include data selection, data cleaning, data transformation, construction of the FP-Tree, and generation of association rules. The FP-Growth algorithm was implemented using the Python programming language on the Google Colab platform. The results indicate that FP-Growth successfully identified 33 valid association rules representing product combinations frequently purchased together, such as strong relationships between Broom and Mop, Detergent and Toilet Brush, as well as bedding items like Pillows, Bedsheets, and Bolsters. These patterns can be utilized to develop product bundling strategies, optimize stock availability, and improve product placement. Thus, the application of the FP-Growth algorithm effectively supports data-driven decision-making to enhance operational efficiency and business competitiveness.]]>
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