<![CDATA[This study aims to develop a web-based system utilizing Support Vector Regression (SVR) to predict motor vehicle spare part demand and optimize safety stock levels at JG Motor Sukabumi. The inventory management faces challenges such as fluctuating demand, supply delays, and overstock/stockout risks. To address these issues, SVR is chosen for its ability to handle non-linear and complex data, providing more accurate predictions than conventional methods. This research employs a descriptive quantitative approach with semi-experimental methods to test the SVR model's effectiveness and web-based system validity. The system features monthly demand prediction, safety stock calculation, historical data visualization, and interactive analytical reports. Development involves user requirement analysis, two-year historical sales data collection, data preprocessing, SVR model training with parameter optimization, and Flask-based integration. Black Box Testing ensures primary functions, such as input validation, prediction processing, and stock recommendation outputs, operate correctly. Results indicate the SVR model achieves high accuracy, reflected by low Mean Absolute Error (MAE) values. The web-based system is user-friendly for managers and operational staff to monitor demand and manage inventory efficiently. Moreover, the system supports strategic decision-making, enhancing JG Motor Sukabumi's operational efficiency and competitiveness in the automotive market.]]>
