<![CDATA[Inaccurate production planning can lead to overproduction or underproduction, negatively affecting a company's operational efficiency. This study aims to apply the Prophet method to predict monthly production quantities and to develop a web dashboard as a medium for presenting the prediction results at PT. Asaputex Jaya, Tegal City. This study contributes to the development of decision support systems in the manufacturing sector by integrating Prophet-based predictions into an interactive web dashboard. Prophet was selected for its ability to handle time series data with seasonal patterns and trends. The model was evaluated using MAE, RMSE, and MAPE. The results show that Prophet was successfully implemented with an MAE of 94 units, RMSE of 116 units, and MAPE of 2.01%, which falls into the highly accurate category. Analysis of production patterns from 2021 to 2025 shows that the production peak shifted from May to March, while the lowest point varied between February and June, with the 2026 prediction estimating a peak in March and the lowest point in June. The web dashboard system was successfully developed using React.js, FastAPI, and PostgreSQL, presenting the prediction results in the form of charts, tables, and reports, with all features functioning without errors based on Black Box Testing. This study demonstrates that Prophet is suitable for seasonally patterned production data, and its integration into a web dashboard supports data-driven production planning.]]>
