PENERAPAN ALGORITMA APRIORI PENGOLAHAN DATA MINING DALAM MENGIDENTIFIKASI PRODUCT BUNDLING RICH PETSHOP - Joesran, Aurelia Berliana; Arianti; Muawwal, Ahyar (STMIK KHARISMA Makassar, 2025)
Rich Petshop has not utilized a priori algorithms in data mining to analyze buyer transaction data and optimize sales operations. The aim of this research is to apply an a priori algorithm to identify product bundling combinations and find association patterns that show relationships between products that are often purchased together. The a priori algorithm process includes data transformation, determining minimum support and confidence values, forming association rules, and finding lift ratio values. Sales data for 1 year (June 2023 - May 2024) was processed using Microsoft Excel and Rapidminer. With a minimum support value of 2% and a minimum confidence of 20%, several important association rules are produced. For example, the relationship between