PENERAPAN METODE ALGORITMA APRIORI TERHADAP DATA TRANSAKSI PENJUALAN (STUDI KASUS: TOKO FASENTRO FANCY)

Determining the combination of items and the layout of goods based on consumer buying tendencies is one solution for Fasentro Fancy Stores in developing marketing strategies so as to increase sales in stores. The algorithm that can be used to find any combination of items that are often purchased simultaneously at a time is the Apriori Algorithm, this priori algorithm is a type of data mining rule, which is to determine associative rules between a combination of items, the result of associative rules from consumer purchase analysis The shop owner can arrange the placement of his items or design a marketing campaign by giving discounts on combinations of these items. Based on data on sales transactions at fancy fasentri stores, analysis is carried out using a priori algorithm with minimum support parameters of 33.33% and minimum confidence of 80%. In this study, 4 itemset combination association rules were generated, namely 2 itemset combinations and 2 combinations of 3
itemsets that met the predetermined minimum support values and minimum confidence values. Based on the results of the final association rules that meet the highest support value and confidence value, namely if Pranaya (P24TVC), Toyobu Fodu will buy Roberto Cavali