<![CDATA[Micro-enterprises such as coffee shops often face challenges in managing inventory due to fluctuating and unpredictable sales. This can lead to either overstocking or understocking, resulting in financial losses and a decline in service quality. This study aims to design and develop a web-based sales forecasting system using the Single Exponential Smoothing (SES) method to assist micro-business owners in making more accurate sales predictions. The research method employed is Research and Development (R&D), utilizing the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). The system was tested on a case study of the Parralokha coffee shop in Denpasar using sales data from June to July 2023. A smoothing constant of a = 0.1 was applied, and forecasting accuracy was evaluated using the Mean Absolute Percentage Error (MAPE). The results show that the system can generate reliable sales forecasts with low error rates, especially for menu items with stable sales patterns such as French Fries, Moccacino, and Matcha Latte, which achieved MAPE values below 3%. On the other hand, items with fluctuating sales, like Toast Bread and Chocolate Latte, had higher MAPE values, indicating lower forecast accuracy. In conclusion, the developed system effectively supports inventory management and helps micro-enterprise owners make data-driven decisions by providing accurate short-term sales forecasts.]]>
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