Implementasi Algortima Random Forest untuk Memprediksi Penjualan Produk pada Toko Ritel Elektronik di Kota Palembang

Manurung, Anzelly
Rahman, Abdul

MDP Student Conference
Vol 5 No 2 (2026): The 5th MDP Student Conference 2026

This study applies data mining techniques using the Random Forest algorithm to predict product sales at Toko ritel elektronik di kota Palembang Palembang based on product categories. The process begins with an understanding of the business context, followed by data collection, cleansing, and transformation into a format suitable for analysis. Random Forest is chosen for its capability to handle high-dimensional data and provide accurate classification results. The model's performance is evaluated using a confusion matrix to assess classification effectiveness across different sales segments. The findings indicate that the algorithm can effectively identify sales patterns and accurately predict products based on their sales value. This model contributes to supporting operational analysis, optimizing marketing strategies and inventory management, and serves as a foundation for developing intelligent decision support systems in the information technology-based ritel sector.