SMART PRICING BASED ON DATA INTELLIGENCE IN MOON CAFE

Abstract

The availability of transaction data in culinary SMEs offers opportunities for data-driven decision-making, yet it is often underutilized. This study proposes a Smart Pricing and menu bundling recommendation system for Moon Cafe Batam using data intelligence. The research applies the CRISP-DM framework to analyze historical transaction data from a POS system. The FP-Growth algorithm is used to identify frequent itemsets and association rules that represent customer purchasing patterns. These patterns are combined with Cost of Goods Sold (COGS/HPP) calculations to generate menu bundling and pricing recommendations that consider profitability. The system is implemented as a web-based application using React.js, PHP, and MySQL, and evaluated through black-box testing. The results show that the system can support structured menu bundling analysis and pricing simulations, enabling managers to make more objective and data-driven decisions in culinary SMEs.