Development of Web System for Sales Optimization at CV. CS Swalayan using Association Rule Method - Steven Imanuel Naibaho; Yullita Molliq Rangkuti (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 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 "if Selai Srikaya Ngetop is purchased, then Roti Tawar Kupas Ngetop will be purchased" (confidence: 100%; lift ratio: 49.3) and "if Beras Sukaraya Cap Gurih 10KG is purchased, then Minyak Kita Minyak Goreng Sawit 1ltr will be purchased" (confidence: 100%; lift ratio: 16.4). The developed web system successfully passed black-box testing with a 100% success rate. This research contributes by providing a system that enables CV. CS Swalayan will make data-driven decisions to optimize sales strategies, marketing approaches, and inventory management practices.