<![CDATA[The advancement of technology has driven businesses to innovate digitally to improve operational efficiency and effectiveness, including in the food and beverage industry. Two Much Coffee & Roastery is one of the cafes that still operates in a semi-digital manner, recording transactions manually using Microsoft Excel. The cafe faces challenges in optimizing sales strategies, particularly in implementing cross-selling strategies, as there is no system that provides automatic product recommendations. This study aims to implement data mining techniques using the FP-Growth algorithm to identify consumer purchasing patterns from historical transaction data. The algorithm was applied with a minimum support of 0.01 and a lift of 1.0, resulting in 30 association rules. These rules were integrated into a web-based system used by the cashier to support cross-selling strategies. The system not only records transactions but also provides product recommendations based on previous purchasing patterns, which is expected to effectively and efficiently increase the cafe’s sales.]]>
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