Sales Analysis Using Apriori Algorithm in Data Mining Application on Food and Beverage (F&B) Transactions

Abstract

The current business landscape has compelled many companies to compete in boosting their company's revenue, particularly in the F&B sector. Existing sales transaction data has not been fully maximized in determining the business strategy of companies. Therefore, the implementation of data mining is necessary to analyze and explore available data to discover new information that is more beneficial for the company. In this study, we analyze sales transaction data using the a priori algorithm method because this algorithm efficiently handles the data mining process on a large scale with a substantial amount of data. The results of this study indicate that the formed association rules can determine patterns of product purchases that are frequently bought together. The established association rules successfully combine sales transaction data into two-item combinations, namely green tea latte and french fries, with a support value of 16% and a confidence level of 83%. These rules can be used as a reference in determining the company's business strategy.