APPLICATION OF APRIORI DATA MINING METHOD FOR PURCHASING PATTERN ANALYSIS AT UB MART MINIMARKET - Ihsan Ferdy Nurfauzy; Ade Eviyanti; Hamzah Setiawan; Ika Ratna Indra Astutik (Journal of Artificial Intelligence and Digital Economy, 2025)
Objective: The Apriori Algorithm method can be used to see the pattern of purchasing goods, such as what happens in the retail industry, including UB Mart minimarkets. UB Mart is one of the minimarkets that sells daily necessities and is often faced with challenges in increasing sales and revenue. Considering that there is quite a lot of competition in this minimarket, UB Mart is required to be able to think of a marketing strategy so that it is not inferior to competitors and one of them is by analyzing purchase patterns with a priori algorithm. It is hoped that by conducting this research it can find out the purchase pattern from UB Mart transaction data, so that transaction data that was initially just a pile of sales reports can turn into the right business strategy. Method: The Apriori Algorithm method can be used to see the pattern of purchasing goods by analyzing purchase patterns with a priori algorithm. Results: The results of this study resulted in 4 association rules from sales transaction data in 2024 in January and February. For example if someone buys a 600 ml retail Tras, they are likely to also buy Sakha with a confidence level of 23.89%. And the results of this research can provide business strategy advice to minimarket management for stock strategies, placement of goods to bundling promos. Novelty: Transaction data that was initially just a pile of sales reports can turn into the right business strategy through the application of the a priori algorithm to reveal purchase patterns in UB Mart.