<![CDATA[Small and Medium Enterprises (SMEs) still widely use manual production recording, which is prone to errors and does not support rapid performance analysis. This study developed a digital production recording system integrated with forecasting features to support data-driven decision making. The system was built using the Laravel framework and applied the Autoregressive Integrated Moving Average (ARIMA) algorithm to predict production volumes based on historical data from September 2024 to September 2025, with a one-month forecasting horizon. The evaluation was conducted by comparing the prediction results with actual production data. The test results showed that the ARIMA model performed well, with a Mean Absolute Error (MAE) of 21.6 and a Mean Absolute Percentage Error (MAPE) of 1.99%, indicating a low level of prediction error. The integration between the digital recording system and the forecasting model allows production managers to monitor production history in a structured manner, obtain estimates of production for the next period, and accelerate the preparation of operational reports. The scientific contribution of this research lies in the development of a system framework that combines automated recording and predictive analytics in an integrated manner, thus offering a new approach to improving operational efficiency and data-driven decision making in the SME sector.]]>
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