Implementasi Data Mining Untuk Analisa Data Penjualan Cat Menggunakan Algoritma Apriori dan Fp Growth (Studi Kasus PT.Sumbermas Unggul Nastari)

Abstract

PT.Sumbermas Unggul Nastari provides paint products with a variety of brands. Every day there is a sale of goods transactions that result in a lot of sales transaction data that accumulates. Researchers are interested in implementing and then comparing two association rule algorithms, namely a priori algorithm and FP-Growth to provide minimum support information that best suits the need to produce the highest frequent itemsets. The results obtained are, JAC with 66% support, JB with 66% support, and results that meet the minimum 70% confidence requirements such as If you buy JAC you will buy JB with 80% confidence, If you buy JB you will buy JAC with 100% confidence.