<![CDATA[The rapid growth of the food and beverage industry encourages business actors to have innovative sales strategies to increase their sales. This thesis focuses on TERÉ café and Bar Seminyak, which has not utilized its sales transaction data optimally. The main purpose of the preparation is to identify customer purchasing patterns and formulate recommendations for food and beverage menu packages that can increase sales. This thesis uses data mining techniques with Association Rules and the FP-Growth algorithm to analyze sales transaction data at TERÉ café and Bar Seminyak based on customer preferences in five different time sessions. The data used is sales data from July 1, 2023 to June 30, 2024 and the framework used is CRISP-DM. The results of the analysis show that there is a strong combination between “Octopus” and “Burger” in the opening session, a strong combination between “Baked Egg” and “Avocado Toast” or “Tere Toast” in the lunch session, and in the next three sessions there is a strong combination between “Bintang (PACKAGE)” and “B2G3 BINTANG”. These results were obtained from the min support parameters of 0.01, confidence of 0.1 and lift of 2.]]>
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