<![CDATA[Grocery stores as small-scale retail businesses generate large volumes of sales transaction data that have not been optimally utilized to support business decision-making. These transaction data contain valuable information in the form of customer purchasing patterns that can be analyzed using association rule mining techniques. This study aims to integrate the Apriori and FP-Growth methods into a Business Intelligence System to optimize sales strategies in a grocery store.Sales transaction data were processed using the WEKA application by applying two association rule mining methods, namely Apriori and FP-Growth. The Apriori method was implemented with a minimum confidence value of 90%, while the FP-Growth method used a minimum confidence value of 55%. The results show that the Apriori method produces association rules with a higher level of confidence, particularly for combinations of staple products such as rice, cooking oil, sugar, and instant noodles. Meanwhile, the FP-Growth method generates a wider variety of association rules with lower confidence values but offers superior computational efficiency in terms of processing time.A comparative analysis indicates that the Apriori method is more effective in producing highly reliable association rules for specific strategic recommendations, whereas the FP-Growth method is more suitable for exploring overall purchasing patterns with lower computational complexity. The integration of both methods into a Business Intelligence System provides strategic recommendations related to inventory management, product placement, and promotion planning based on customer purchasing behavior. Therefore, the proposed integration of Apriori and FP-Growth is expected to enhance sales strategy effectiveness and improve the competitiveness of grocery stores.]]>
