Implementation Of Data Mining To Find Product Sales Patterns Using The Apriori Algorithm (Case Study: Warung Dini)

In the increasingly fierce business competition, utilizing sales transaction data is crucial to improve business performance, including at Warung Dini. This research aims to identify customer purchasing patterns at Warung Dini by applying the Apriori algorithm, which is one of the effective data mining methods in uncovering associations between items in large transaction data. Using the Apriori algorithm, this research successfully found 12 association rules consisting of 5 combinations of two itemsets and 2 combinations of three itemsets, with a minimum support value of 60%, confidence 100%, and lift ratio 1. These itemset combinations reveal products that are often purchased together, such as Rice and Soy Sauce, as well as combinations of three products such as Rice, Soy Sauce, and Instant Noodles. These findings provide important insights for Warung Dini to optimize marketing strategies, such as designing attractive promotional packages and arranging product placement more strategically to increase sales. In addition, the results of this analysis also help the store in predicting stock needs, thus improving operational efficiency. The implementation of the Apriori algorithm in this study not only provides insight into sales patterns, but also supports more precise and strategic business decision-making, so as to strengthen the competitiveness of Warung Dini in a competitive market.