Penerapan Data Mining Untuk Rekomendasi Paket Produk Menggunakan Algoritma Apriori dan Algoritma FP-Growth

Nevile, Steven
Yulistia, Yulistia

MDP Student Conference
Vol 4 No 1 (2025): The 4th MDP Student Conference 2025

This research utilizes data mining with the Apriori and FP-Growth algorithms to generate product package recommendations for Planty Burger based on sales transaction data. Through data mining-based analysis, Planty Burger can identify consumer preference patterns and provide more relevant recommendations. The research methodology includes Data Collection, Data Preprocessing, Algorithm Implementation, Analysis and Result Evaluation, and Software Development. The use of both algorithms allows for a comparison of results to ensure the validity of the identified patterns. Based on the analysis, it can be concluded that if a customer purchases Matcha Greentea, there is a high probability (75% confidence) that they will also purchase a BBQ Steak Burger. The analysis results are expected to enhance customer satisfaction and sales by providing appropriate product package recommendations.