Implementasi Algoritma Apriori sebagai Association Rule Learning untuk Mengidentifikasi Pola Item Dataset Penjualan - Supriana, I Wayan; Rahning Putri, Luh Arida Ayu (Jurnal Buana Informatika, 2025)
Retail store competition is becoming more intense, so marketing and product arrangement are crucial for shopping efficiency, maintaining comfort, and increasing profits. This study analyzes consumer shopping habits for goods in each transaction through market basket analysis. The Apriori algorithm is a common technique for finding frequent item search techniques in building association rules, namely the relationships between item combinations in a dataset. The aim is to implement the Apriori algorithm as an association rule learning method to identify patterns within sales data. The Apriori association rule is compared to the frequent pattern growth algorithm, which finds the most frequently occurring patterns in a dataset. Based on the tests, the average lift ratio for the Apriori algorithm is 1.58, while for the frequent pattern growth algorithm, it is 1.28. This indicates that the Apriori algorithm performs better than the frequent pattern growth algorithm.