OPTIMASI PENJUALAN ROTI MENGGUNAKAN MODEL ENSEMBLE MACHINE LEARNING PADA TOKO ROTI SYAKILA

Improving the effectiveness of bread sales requires a data-driven approach capable of identifying historical sales patterns and accurately estimating sales volumes. This study applied the Extreme Gradient Boosting (XGBoost) algorithm as an ensemble machine learning model to predict bread sales at Syakila Bakery. The data consisted of six months of historical sales data, from July to December 2025, covering sweet bread, white bread, snack bread, and sponge cake products. The research process used the CRISP-DM methodology, consisting of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The system was developed using PHP as the application interface, Python for the modeling process, and MySQL as the database. Model evaluation was conducted using Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE). The evaluation results showed a MAPE of 1.3% and an MSE of 0.21. These results indicate a low prediction error rate, suggesting that the XGBoost model can support more measurable sales planning and facilitate decision-making.