Application Of Data Mining Using Apriori Algorithm To Know Customer Purchase Patterns (Case Study: Timbul Jaya Motor)

Abstract

Timbul Jaya Motor is a trading business that sells four-wheeled vehicle spare parts. Timbul Jaya Motor has problems such as accumulation of transaction data which is only used as an archive. Increasingly tight competition in the sale of four-wheeled vehicle spare parts makes it difficult for Timbul Jaya Motor owners to find a strategy that can increase sales and marketing of four-wheeled vehicle spare parts sold, one of which is by utilizing four-wheeled vehicle spare parts sales data. This research uses data mining with an a priori algorithm, so that later the results can be used to develop increased sales and marketing of four-wheeled vehicle spare parts products and to understand purchasing patterns for four-wheeled vehicle spare parts products. Apriori is one of the most famous algorithms in data mining for finding data patterns or data occurrence/frequency patterns. The usual Apriori algorithm is used to find customer purchasing patterns at a minimarket based on purchase transactions. The result of this research is that if you buy a "Lower Solar Filter" you will buy an "Upper Solar Filter" with a support value of 26% and a confidence value of 81.25%. If you buy "Front Brake Pads" you will buy "Rear Brake Pads" with a support value of 24% and a Confidence value of 80%. If you buy "Valve adjustment bolt" you will buy "Valve adjustment nut" with a support value of 26% and a confidence value of 72.22%. If you buy "Wiper" you will buy "Wiper Arm" with a support value of 30% and a Confidence value of 71.43%. And Timbul Jaya Motor can set future sales strategies with 4 rules obtained from transaction data which is processed using an a priori algorithm.