Analisis Pola Transaksi Pelanggan Menggunakan Algoritme Apriori

Abstract

Diengva is a shop that sells various kinds of goods such as household items, household appliances, accessories, flower buckets, clothes, bags, shoes, cosmetics, and others. The large number of purchase transaction data in diengva can be used to analyze customer behavior in purchasing goods. Apriori algorithm is one of the algorithms in the field of data mining for extracting association rules. This study applies the apriori algorithm to find customer buying patterns in diengva store sales transaction data using rapid miner. The rules resulting from the application of the apriori algorithm can be used as a basis for stocking items that meet the minimum support and minimum confidence values. Items that meet these rules are eyelashes, eyelash glue, soft lens, and soft lens water. The confidence value of the relationship between two items can be high so that the results of these rules can be used as the basis for stocking.