Implementasi Data Mining Menggunakan Algoritma Apriori Untuk Menemukan Frequent Itemset Penjualan Sneakers

Abstract

Every small or large company that wants to stay afloat in an increasingly fierce business competition requires the right sales strategy, including at the Yasa Collection Sport Store. One way is to perform data mining analysis on the sales transaction database using the a priori algorithm of the association rule method. This method makes it possible to find combinations of items that often (often) appear from a collection of items (itemset), so that store management knows market conditions, consumer tastes and sales patterns. Based on the results of research and data analysis conducted with a minimum support value of 0.33 and a minimum confidence value of 0.80, three association rules were obtained, in which the frequent itemset information that had been found using the a priori algorithm, could be utilized by the store management in determining sales strategies, such as discount promotion, packaging and stocking goods.