Implementasi Algoritma Apriori untuk Menentukan Pola Transaksi Penjualan Berbasis Web

The Apriori algorithm is an algorithm that is well known for searching frequent itemsets using the association rule technique. The calculation of the Apriori algorithm uses minimal support and minimal confidence to determine the limit for calculating goods. The a priori algorithm functions to determine the pattern of sales of goods that are often purchased together by customers. The history of sales transactions owned by a store can be calculated for its frequent itemset pattern by using an a priori algorithm so that customers can find patterns of items that are often purchased simultaneously by customers. Therefore, the a priori algorithm is very important to be used by shop owners because it can determine sales strategies and the placement of goods that are often purchased simultaneously by customers. In this study, the authors succeeded in calculating a sales transaction by determining a minimum support limit of 10% and a minimum confidence of 10%. With the minimum support and minimum confidence that has been set by the author to see the results of the a priori algorithm for sales, then the results of 2 combinations of itemsets that meet the calculation requirements are obtained.