Baby Supplies Sales Prediction System using the Single Exponential Smoothing Method at Little Queen Baby Shop - Silvia Agustin; Miftahus Sholihin; Agus Setia Budi (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2026)
The increasing demand for baby equipment in Indonesia in recent years has created significant business opportunities for the retail sector, including Little Queen Baby Shop. However, seasonal fluctuations in demand often lead to stock management problems such as overstock and out of stock, which affect storage costs and customer satisfaction. This research aims to design and develop a sales prediction system for baby products using the Single Exponential Smoothing (SES) method as a solution to minimize forecasting errors and support data-driven decision-making. The research method involved collecting secondary sales data from January to November 2024, which was then processed using the SES algorithm with a smoothing parameter (α) to determine the optimal prediction values with the lowest error rate. The system was developed as a web-based application using PHP programming language and MySQL database, equipped with features such as transaction recording, stock management, sales analysis, and prediction reports for upcoming periods. The implementation results show that the SES-based prediction system provides sufficiently accurate forecasts, as indicated by low values of Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). This system enables Little Queen Baby Shop to optimize stock management, reduce the risk of losses due to excessive or insufficient inventory, and improve both operational efficiency and customer satisfaction.