Implementation of Data Mining to Determine Menu Combination Selection Patterns Using Apriori Algorithm (Case Study: The Coffee Theory)

Abstract

The Coffee Theory is a business focused on selling coffee and food that is looking for effective promotional tactics for its sales. So far, The Coffee Theory only stores sales data in the Moka application without knowing that the sales data can be analyzed and utilized. This sales data can help this business determine sales packages so that promotional efforts will be more targeted. Manually, the process of analyzing customer purchasing patterns certainly requires more time and process. Therefore, data mining implementation research was conducted to determine the pattern of menu combination selection using the apriori algorithm. Designing applications that can understand menu selection patterns by customer purchases with the Data Mining method, namely the Apriori Algorithm to produce association rules to find out how influential an item affects other items. From the business analysis process to the design stage, a web-based application with the Apriori Algorithm was built. Datasets that have been obtained from The Coffee Theory are taken sample data to perform the calculation process manually and the calculation process with RapidMiner Studio, this process is used to find out the flow of calculations with the Apriori Algorithm so that it can be implemented in the application that will be made properly and correctly. This developed web-based application analyzes transaction data by applying a date range to the data to be analyzed and setting the desired minimum support value and minimum confidence value.