Implementasi Association Rule Dalam Menganalisis Data Penjualan Sheshop dengan Menggunakan Algoritma Apriori

Abstract

Sheshop is a business activity engaged in the field of hamper making services. Since the last 2 (two) years, sales transactions in Sheshop have been increasing, the transaction data is only used as a report and is not used to regulate business strategies. The transaction data should be used to see the attachment of each type of product purchased by the customer simultaneously. The amount of sales transaction data on Sheshop can be used as an analysis of customer behavior in making purchases of hampers at Sheshop. This study performs data analysis by implementing the Apriori algorithm method because this algorithm handles data mining processes quickly on large amounts of data, from the results of this study Sheshop can make decisions on what items need more inventory compared to other items by looking at the value confidence and support by using the RapidMiner application. The results of this study indicate that the association rule formed from 568 Sheshop sales data uses a minimum support value of 10% and a minimum confidence of 50% produces 6 (six) association rules with a confidence value of 58% to 75% with all rules having a positive correlation level. Based on the 6 (six) association rules obtained, 2 products are often purchased at the same time, namely the Koran and tasbih with a confidence value of 75%.