Implementasi Algoritma Random Forest untuk Prediksi Roti Terlaris pada Toko Fun Bread Berbasis Web - Arjiansyah, Abiem Mahmud; Agustian, Bobi (shofanah Media Berkah, 2026)
This study aims to develop a best-selling bread prediction system for Fun Bread Bakery using the Random Forest algorithm, implemented in a web-based application. The main issue faced by the store is the difficulty in accurately determining the types of bread most favored by customers, as stock planning is still done manually and not based on historical data. The Random Forest method was chosen because it can perform classification with high accuracy and handle complex data. Historical sales data was used as a dataset to train the prediction model. The system was developed using the Laravel framework, MySQL database, and Visual Studio Code as the development environment. The outcome of this project is a web application capable of processing sales data and providing recommendations for types of bread that have the potential to become best-sellers. With this system, it is expected that stock management at Fun Bread can be carried out more effectively and efficiently, while also supporting data-driven decision-making.