Association Rule to Increase Sales Using the Apriori Algorithm Method

Abstract

The Apriori algorithm is a data mining technique used to find relationship patterns between items in a transaction dataset. In this context, the Apriori algorithm will be used to identify products that are often purchased simultaneously by customers. By understanding these purchasing patterns, companies can design more effective marketing strategies, such as strategic product placement, bundling package offers, and special promotions. This research involves several stages, starting from collecting sales transaction data, data preprocessing, applying the Apriori algorithm, to interpreting the results. The transaction data used is taken from the sales database of a retail store during a certain period. After the data is processed, the Apriori algorithm is applied to identify frequent itemsets and form association rules. The results of this research show that there are several significant purchasing patterns, such as a combination of product A and product B which are often purchased together. By applying data mining using the a priori algorithm method, you can find out which products sell the most. From the results of manual calculations it was found that consumers who bought RB 1060 would buy RB 1099 with 81% confidence, whereas using WEKA it was found that consumers who bought RB 1060 would buy RB 1099 with a confidence value of 82%.