Apriori Algorithm in Social Commerce Utilization and Market Basket Analysis in Local Retail - Mahardika Inra Takaendengan; Nolin Jufika Kabaitan; Marline Sofiana Paendong (Journal of Information Technology, Software Engineering and Computer Science (ITSECS), 2026)
Social commerce platforms provide micro-enterprises with low-cost channels to expand market reach, yet systematic evaluations of their operational effectiveness and transaction data utilization remain limited. This study examines Facebook Marketplace utilization, identifies factors affecting its effectiveness, and mines transaction patterns to formulate data-driven sales strategies. A mixed-methods descriptive exploratory design was employed. Qualitative data were collected through interviews and observations with store personnel and customers to identify operational constraints. Quantitative data comprised 200 sales transaction records, with 72 originating specifically from Facebook Marketplace, analyzed using the Apriori algorithm. The results indicate that platform adoption correlated with a 50% increase in transaction volume and a 98.43% increase in total revenue. The Apriori analysis generated six valid association rules. The strongest product association emerged between Jeans and Belts, yielding a lift ratio of 3.13. These findings demonstrate that social commerce functions as a critical discovery channel that alters operational workflows and generates structured transaction logs. Micro-enterprises, for example, could use these findings to plan targeted product mix-ups and get better synchronization of inventory. Lastly, the work done in this paper offers, among other things, a concrete solution for traditional, small-scale fashion business owners aiming at moving beyond using social media just as the platform for advertising and towards turning their streams into valuable information resources that would help them carry out strategic stock planning and online sales more efficiently.