Development of Web System for Sales Optimization at CV. CS Swalayan using Association Rule Method - Steven Imanuel Naibaho; Yullita Molliq Rangkuti (Yayasan Kita Menulis, 2026)
CV. CS Swalayan encounters challenges related to declining consumer purchasing power and the underutilization of transactional data for analyzing customer purchasing patterns. This study aims to develop a web-based system employing Association Rule methodology with the Apriori algorithm to optimize sales performance, identify top-selling products, and determine frequently co-purchased product combinations. The research methodology encompasses the collection of 296 sales transaction records for basic commodity products from CV—CS Swalayan during January 2025, followed by data preprocessing procedures. The Apriori algorithm is implemented with minimum support and confidence thresholds set at 0.01 and 0.3, respectively. The web-based system is developed using Python with the Flask framework for backend functionality, MySQL for database management, and validated through black-box testing methodology. The findings reveal the generation of 14 valid and robust association rules, notably