Analysis of Puchase Patterns on Office Stationery Sales Data using Apriori Algorithm

Wahyudi, M. Ilham Setyo
Nurdiyansyah, Firman
Kristianti, Dini

JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Vol. 2 No. 3 (2025): October

This study analyzes purchasing patterns in office stationery sales using the Apriori algorithm, a data mining method for generating association rules and frequent itemsets. The research examines transaction data to identify combinations of frequently purchased items, aiming to improve inventory management and marketing strategies. The Apriori algorithm calculates metrics such as support, confidence, and lift to determine strong associations between items. Results indicate key purchasing patterns, such as frequent copurchases of notebooks and pencils, which inform targeted promotions and stock planning. The findings highlight the potential of data-driven decisionmaking to enhance business efficiency and customer satisfaction in the retail sector.