<![CDATA[This study aims to apply data mining to identify customer purchasing patterns and generate menu package recommendations based on sales transaction data from Jala Seafood. The CRISP-DM methodology is employed, consisting of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The Apriori algorithm is applied using a minimum support of 6% and a minimum confidence of 60% to extract relevant association patterns. The findings identify 11 valid association rules, with the highest lift ratio of 3.6823 for the combination {Kangkung Ongseng, Kerapu Saos Padang, Nasi Putih} → {Es Teh Manis}. These results demonstrate that the Apriori algorithm effectively uncovers hidden purchasing patterns within the sales data. Furthermore, the developed dashboard facilitates visualization of the association patterns and assists the restaurant in designing sustainable and data-driven menu packages. This approach is expected to support increased sales value and provide menu recommendations aligned with customer preferences ]]>
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