<![CDATA[This study is motivated by the problem faced by Toko Polirindo, where sales transaction data are stored only as archives and have not been utilized for analytical purposes, resulting in unstable product availability, recurring stock shortages, and difficulties in predicting customer purchasing behavior; therefore, this research aims to identify patterns of item associations that frequently occur together by applying the Association Rule Mining method using the FP-Growth algorithm, which is recognized for its ability to extract frequent itemsets efficiently without the need to generate candidate combinations as in the Apriori algorithm. The dataset consists of sales transactions recorded from January to September 2025. It undergoes several stages, including preprocessing, binary transformation, and analysis using RapidMiner to generate frequent itemsets and association rules, evaluated using support, confidence, and lift metrics. The results reveal that item 3 consistently appears as the most dominant consequent across almost all generated rules, with confidence values ranging from 0.322 to 0.347, indicating that this item is most strongly associated with other items and frequently appears as a complementary product in customer transactions. These findings provide practical contributions by offering insights to optimize stock management, improve product placement, and develop promotional strategies based on actual purchasing patterns, while also demonstrating that the FP-Growth algorithm is an effective analytical tool to support data-driven decision-making aimed at enhancing operational efficiency and customer satisfaction in retail environments.]]>
