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

Widi Setiana (Universitas Nusa Mandiri)
Della Andina (Universitas Nusa Mandiri)
Nabhilah Deviani (Universitas Nusa Mandiri)
Numan Musyaffa (Universitas Nusa Mandiri)

Ladang Artikel Ilmu Komputer
Vol 1 No 2 (2021): Desember 2021

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.