Implementation of the Max-Miner algorithm for product recommendations in Café Lo Aja

Abstract

Cafe Lo Aja is a company that sells food and drinks. The business process carried out by this company is buying goods from suppliers and selling products to customers. Sales transaction data will produce heaps of data that get bigger and bigger, so that it can cause new problems. The purpose of this research is to make an application to process a sales transaction data so that it can produce information about consumer purchasing patterns which will be used to help the Cafe Lo Aja to make business decisions. This study uses Cafe Lo Aja sales transaction data in 2019 with the Data Mining Market Basket Analysis method and the Max-Miner Algorithm. This research produces data which is an association rule from a collection of sales transaction data. So that by knowing the pattern of purchasing these products, the cafe manager can predict future market needs that can take into account what stock items must be reproduced and what items should be reduced because of the small percentage of interested ones. By knowing the results of the association, the manager can adjust the layout of the product menu to be better because products that are often purchased will be placed close together. Suggested application development that can be done in further research is the system can be developed into a mobile application, so consumers can order products on their smartphone devices.