<![CDATA[Vehicle maintenance is an important aspect to ensure optimal performance and vehicle life. Conventional maintenance approaches based on time or mileage (Time-Based Maintenance) are often ineffective, because they do not consider the actual condition of the vehicle. Predictive Maintenance supported by machine learning algorithms offers a more accurate solution in detecting potential vehicle damage before failure occurs, so that maintenance can be carried out according to actual needs. This study aims to develop a machine learning-based predictive maintenance model with a case study at the Payung Auto Solution workshop, which leads to the repair of Nissan, Datsun, and other vehicle brands. The methods used in this study include collecting operational data and vehicle maintenance history at Payung Auto Solution. This data is analyzed and processed using machine learning algorithms, such as Random Forest and Neural Network, to build a predictive model that is able to identify damage patterns in vehicle components. This model is tested and evaluated using prediction accuracy metrics, to determine the effectiveness of the model in predicting maintenance needs.]]>
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