Implementation of the Apriori Algorithm to Identify Sales Patterns at YES 248 Store - Damiana Desrimawarni Waruwu; Porlina Hulu; Berto Nadeak (STMIK Methodist Binjai, 2026)
YES 248 Store generates daily sales transaction data that have primarily been retained as administrative records, leaving relationships among products underutilized in decision-making. This study identifies consumer purchasing patterns by applying the Apriori algorithm to 20 transactions recorded in April 2026 and involving five products. The data were cleaned, product names were standardized, and each transaction was transformed into an itemset. The minimum support and minimum confidence thresholds were set at 30% and 60%, respectively. The analysis produced three frequent 1-itemsets: Milo Activ-Go with 80% support, 5Days Croissant Choco with 65%, and Ovaltine UHT with 45%. The combination of 5Days Croissant Choco and Milo Activ-Go achieved the highest pair support at 60%, whereas the three-product combination achieved 35% support. The highest-confidence rule was 5Days Croissant Choco, Ovaltine UHT => Milo Activ-Go at 100%, while the strongest single-antecedent rule was 5Days Croissant Choco => Milo Activ-Go at 92.31%. These findings can support product bundling, shelf arrangement, and inventory prioritization. Nevertheless, the results remain exploratory because the number of analyzed transactions is limited.