IMPLEMENTASI METODE RANDOM FOREST UNTUK KLASIFIKASI PENJUALAN PRODUK SABUN PALING LARIS

This research aims to analyze soap product sales data in a supermarket to understand sales patterns and categorize products based on their sales levels. Considering the multitude of soap products, the supermarket finds it difficult to conduct research on the best-selling soap products. The data used includes 4,694 soap product sales data from January 2022 to December 2023, with variables such as type, brand, price, and quantity sold. In this study, the Random Forest method is used to classify soap products into four categories: not popular, less popular, popular, and most popular. The process of data analysis and processing was carried out using Google Colaboratory with the Python programming language. Based on the evaluation results, the produced model has an accuracy of 94.6%, which indicates that this method is effective in classifying products based on sales levels. The results of this research are expected to help supermarkets optimize inventory management and design more targeted marketing strategies to increase profits. With this classification, supermarkets can focus more on products that have the potential to contribute greater profits.