IMPLEMENTASI DATA MINING UNTUK MENCARI KETERHUBUNGAN ANTAR ITEM PADA DATA TRANSAKSI PENJUALAN MENGGUNAKAN ALGORITMA APRIORI (STUDI KASUS : THE CANOPY KAFE TANJUNGPINANG)

Abstract

The Canopy cafe has a lot of sales transaction data from month to month. This data is only used for daily and monthly reports by The Canopy cafe. But after that the sales transaction data is no longer used and stored in the database. The researcher then builds a system or application by utilizing data mining techniques using a priori algorithms to obtain information in the form of interrelationships between items or association rules from sales data of The Canopy cafe. System or application test results by inputting 0.07 for minimum support and 0.20 for minimum confidence, namely the formation of 7 association rules with the strongest rule being Fruit Tea Classic Drink (if consumers buy Fruit Tea then buy Classic Drink too) with a support value of 0.08 and the confidence value is 0.33 followed by the lift value of 1.256 and the cosine value of 0.317 and conviction of 0.796. These results can be used by the management of The Canopy cafe to determine its business strategy such as making product recommendations, discounts, packages, and others.