Implementasi Perbandingan Algoritma Apriori Dan FP-Growth Untuk Mengetahui Pola Pembelian Konsumen Pada Produk Panel Di PT Surya Multi Perkasa Movinko - Diego Armando Pratama Putra; Tresna Maulana Fahrudin; Natalia Damastuti (Systemic: Information System and Informatics Journal, 2020)
Some companies have not used much consumer purchase transaction data as one of their sales strategies, this transaction data contains what items are often bought by consumers in one purchase transaction at a different time and structure. If the transaction data is analyzed and explored in more depth, the company will gain insight into consumer purchase patterns analysis and be profitable for the company. In this research, an analysis of consumer purchase transaction data was carried out using Apriori algorithm and FP-Growth, both of which are association rule method group that aims to determine consumer purchasing patterns. The data used in this study were obtained from panel product purchase transaction data at PT Surya Multi Perkasa Movinko. The transaction data consist of 23 types of product items and 492 transactions. The experimental results of this study showed that the best performance of Apriori algorithm with a support factor of 0.0054 and a confidence factor of 0.30 generating 12 association rules, while the best performance of FP-Growth algorithm with a supporting factor of 2 and a confidence factor of 0.7 generating 9 association rules.