<![CDATA[TB. Sami Jaya is a construction materials provider company offering key components like sand, nails, cement, iron, heavy steel, and electronic products for infrastructure construction projects. In the era of rapid technological development, the company faces the challenge of making accurate sales predictions. This study aims to develop a sales prediction system using Support Vector Machine (SVM) to help TB. Sami Jaya optimize the supply of construction materials based on existing transaction data, collected from November to December 2023. By identifying relevant trends and patterns, the goal is to generate more reliable projections, thereby reducing the risk of inventory excess or shortage, which increases operational costs and decreases customer satisfaction. The model's performance is evaluated by comparing the prediction results with actual data using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The Rational Unified Process (RUP) method is employed for information system development. The results indicate that using SVM for predicting sales of construction materials is effective and helps optimize inventory management.]]>
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