ANALISIS PENERAPAN DATA MINING DALAM PENENTUAN TATA LETAK BARANG MENGGUNAKAN ALGORITMA APRIORI DAN FP-GROWTH

Setting the layout of merchandise in each store window greatly affects consumer interest in shopping. To increase self-service sales, a strategy is needed to achieve this, one of which is to systematically arrange the layout of goods on merchandise shelves. The method used for implementing the layout of goods is to compare the performance of the Apriori Algorithm and the FP-Growth Algorithm in the data mining process using the Rapidminer Studio Educational Version 9.10.011 tools to obtain more accurate results. The data sample used is sales data at the Mohare Supermarket, which is tested to understand the association patterns generated by each method. Based on the test results with a minimum support of 20% and a minimum confidence of 70%, the Apriori Algorithm produces 10 rules with a support of 0.32258605 and an accuracy of 12.8%, while the FP-Growth Algorithm produces 78 rules with a support of 2.51612903 with an accuracy of 780%. Thus, the FP-Growth Algorithm can be stated to have a high degree of accuracy in generating association rules when compared to the Apriori Algorithm.