Data Mining Using Apriori Algorithm and Linear Regression in Product Recommendations

The utilization of data mining technology has now been widely applied in large companies in various aspects. But unfortunately, many businesses in Indonesia have not used data mining technology to improve future strategic business plans. Based on the problems encountered, the problem is solved by utilizing processed transaction data to provide product recommendations or related superior menus. This research aims to use data mining in improving restaurant services, especially in providing food recommendations. The method used is a prototype in Problem Identification Process, analysis, design, system prototyping, and system testing. The results of this study are a priori recommendations for products in the form of associative rules or consumer transaction patterns by collecting transaction data. In this research we apply apriori algorithm with a minimum support value of 50, minimum confidence value of 75%, and 1 for lift test value. User Acceptance Testing of 6 variables with ten questions regarding effort expectancy, social influence, performance expectancy, use behavior, supporting facilitating, and behavioral intention produces an average index of 86.4%. The lift test value above 1.00 is valid as valid data, then displayed as a product package recommendation in the android application.