Penerapan Data Mining pada Penjualan Ikan Gembung Menggunakan Algoritma Apriori - Silvia Lestari; Dani Manesah (International Forum of Researchers and Lecturers, 2026)
The development of digital technology has increased sales activities and generated large amounts of transaction data. However, transaction data is often only used as an archive and has not been optimally utilized to support business decision-making. This study aims to apply data mining techniques using the Apriori algorithm to discover consumer purchasing patterns based on mackerel fish sales transaction data.The research method used is a quantitative method with a data mining approach. The data used consists of 15 mackerel fish sales transactions. The research stages include data cleaning, data transformation, itemset formation, frequent itemset searching, and association rule formation based on support and confidence values. This study uses a minimum support value of 30% and a minimum confidence value of 50%. The results show that the Apriori algorithm successfully identifies relationships between items in sales transactions. Based on the frequent itemset formation process, a 2-itemset combination was obtained, namely Jumbo and Small-sized mackerel fish, with a support value of 33.33%. The association rule results show that customers who purchase Jumbo-sized mackerel fish tend to purchase Small-sized mackerel fish with a confidence value of 55.56%. Likewise, purchasing Small-sized mackerel fish is also associated with purchasing Jumbo-sized mackerel fish with a confidence value of 55.56%.