PENERAPAN ALGORITMA APRIORI PENGOLAHAN DATA MINING DALAM MENGIDENTIFIKASI PRODUCT BUNDLING RICH PETSHOP - Joesran, Aurelia Berliana; Arianti; Muawwal, Ahyar (JTRISTE, 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 "Pasir Chiro Plus" and "Cat choize adult" and "Furlove Pouch 80gr" and "Cou cou pouch 85gr". These association rules provide valuable insights for Rich Petshop to craft innovative product bundling packages, increase customer interest, drive sales growth and strengthen market position.