Penerapan Association Rule Menggunakan Algoritma Apriori untuk Rekomendasi Strategi Penjualan pada UMKM Toko Pempek - Putri Salsabilah; Ummu Farida Muthmainnah; Zaskia Aulia Wulandari; Talitha Zafirah; Ken Ditha Tania; Alsella Meiriza (Universitas Methodist Indonesia, 2026)
Pempek Ceria SME has a growing number of daily sales transactions; however, these data have not been optimally utilized to support sales strategies. This situation highlights the need for transaction data analysis to understand customer purchasing patterns and develop more effective promotional strategies. Therefore, this study focuses on applying association rules using the Apriori algorithm to provide sales strategy recommendations for Pempek Ceria SME. The analysis was conducted using RapidMiner software on 317 transactions from October to December 2025, with a minimum support of 15% and a minimum confidence of 65%. The results show two association rules that meet these criteria: the combination of Pempek Adaan and Orange Juice, with a support of 28% and confidence of 72%, and the combination of Pempek Kapal Selam and Sweet Iced Tea, with a support of 27% and confidence of 70%. These findings indicate that the association rule method based on the Apriori algorithm can identify relationships between menu items frequently purchased together. By understanding these purchasing patterns, Pempek Ceria SME can optimize bundling strategies and product recommendations to improve promotional effectiveness and sales.