<![CDATA[The culinary industry is experiencing rapid growth, encouraging businesses such as Shans Juice Cafe to continue innovating in order to stay competitive. This café offers a variety of beverages and side dishes, but faces challenges such as sales imbalances between menu items and the absence of menu package recommendations, making it difficult for customers to choose product combinations that align with their preferences. This study aims to identify the relationships between products frequently purchased together and generate menu package recommendations to optimize sales. The method used is Market Basket Analysis with the FP-Growth algorithm, which can efficiently identify association patterns without generating candidate itemsets. The research methodology employs the Cross Industry Standard Process for Data Mining (CRISP-DM) approach, consisting of six stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. This study utilized 16,422 transaction data with a minimum support of 1% and a minimum confidence of 15%, resulting in 42 association rules, 33 of which had a lift ratio >1, indicating a positive correlation between items. Based on these results, six menu package recommendations were developed with appealing names such as Shans Berry Smooth and Shans Kribo Fun. These recommendations not only reflect customer purchasing patterns but also support cross-selling strategies to increase transaction value and sales distribution across products]]>
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