Pola Pembelian Produk Parfum Menggunakan Algoritma Apriori Berdasarkan Data Mining Rule Asosiasi

As technology evolves and trends change, perfume products continue to face challenges to remain relevant and attractive to an ever-evolving market. Data mining allows companies to identify customer segments based on consumer behaviors, preferences, and characteristics. The purpose of this study is to identify the purchase pattern of perfume products based on the tendency of consumers to buy products at the same time. The method used in this study is quantitative, based on transaction data. The programming language used in this study is PHP 5. The use of an a priori algorithm makes it easier to determine the association rules based on the support and confidence values. The association pattern is formed with a minimum of 15% support. It is found that the most frequently sold goods have a total of 5 item transactions, so the most frequently purchased perfume product itemsets at the same time are Jasmine and Fantasy.