Product Recommendation Information System Using the Apriori Algorithm on Sales Transaction Data of Shoe Store X - Devie Hartanti; Candra Mecca Sufyana; Ratnanto Aditiarno (SYSTEMIC RESEARCH: Systemic and Information Technology Journal, 2026)
The advancement of information technology has encouraged retail business owners to utilize transaction data as a basis for business decision-making, particularly in determining sales strategies and product recommendations. Shoe Store X possesses continuously growing sales transaction data; however, this data has not yet been optimally utilized to identify customer purchasing patterns. This study aims to design a product recommendation information system using the Apriori algorithm applied to the sales transaction data of Shoe Store X. The Apriori algorithm is used to discover patterns of relationships between products based on support and confidence values, thereby generating association rules that reveal the tendency of products to be purchased together. The research method employed includes the collection of sales transaction data, analysis of system requirements, system design, implementation of the Apriori algorithm, and testing of the recommendation results. The system developed is expected to help the store provide product recommendations to customers, formulate promotional strategies, arrange product placement, and improve sales effectiveness. The result of this research is an information system capable of processing transaction data into more targeted and user-friendly product recommendation information for store owners. With this system, Shoe Store X can utilize historical sales data as a basis for more effective, efficient, and data-driven decision-making.