Association Rule Mining with FP-Growth for Cross-Selling Recommendation in a Culinary MSME - Bagas Ramadhan; Rudolf Januar; Firman Noor Hasan (Journal of Computers and Digital Business, 2026)
The rapid growth of digitalization in Micro, Small, and Medium Enterprises (MSMEs) has led to a significant increase in daily sales transaction data. However, most culinary MSMEs utilize this data merely as transaction records rather than processing it into valuable insights for business decision-making. Kedai Mie Rahma faces challenges in determining promotional strategies, designing menu packages, and managing raw material inventories due to the lack of systematic analysis of customer purchasing patterns. This study aims to extract valid association rules from sales data using the FP-Growth algorithm to provide data-driven cross-selling recommendations. The methodology follows the Knowledge Discovery in Databases (KDD) framework, analyzing 2,500 historical transactions from the Point of Sales (POS) system. The results successfully identified significant purchasing patterns, with the strongest valid rule being the association between Sweet Snacks and Ngemie (Support: 6.4%, Confidence: 79.9%, Lift Ratio: 1.100). The findings transition the use of FP-Growth from a routine application into a practical decision-support tool, providing the MSME management with a strategic basis for menu bundling, cross-selling opportunities, and targeted inventory planning.