Implementation of association rule using apriori algorithm and frequent pattern growth for inventory control

Abstract

Business success is a business that is able to compete and grow keep abreast of developments in the business world. Especially in the retail sector, where competition is getting tighter. Business owners need to pay attention to the layout of goods and stock management to improve service and meet consumer needs because consumers often have difficulty in finding goods. On the other hand, shortages and excess stock often occur due to lack of goods management. Based on these problems, appropriate techniques are needed for the management of goods supply, one of which is to apply techniques found in the branch of science. Data mining is a technique of association rules. This study aims to find patterns of placement and purchase of goods in generating Association Rule using FP-Growth algorithm. The dataset in this study used data on sales of goods in clothing stores. The results of the study of 140 transactions there are 24 association rules consisting of 7 association rules with 2-itemsets and 17 association rules with 3-itemsets that most often appear in transactions. Based on the order of the highest support value, namely CKJ→STX^LK with a support value of 67%, while the highest confidence value, there are 3 association rules that get the same value, namely STX^CKJ→LK, STX^CAK→LK, STX^RI→LK with a value of 100%. Thus, the rules of association produced by the frequent itemset algorithm, FP-growth, can serve as decision support for the sales of goods in small and medium-sized retail businesses