<![CDATA[In the application-based transportation industry, selecting the right vehicle for an online driver is a crucial factor in enhancing operational efficiency and income. The choice of a suitable vehicle is influenced by various factors, such as fuel efficiency, maintenance costs, purchase price, passenger capacity, and safety features. This study employs the action research method, starting from the planning stage to the evaluation stage. Meanwhile, the data processing method used to determine the most suitable vehicle for online drivers, based on the predefined criteria, applies the Naïve Bayes algorithm. The Naïve Bayes algorithm was chosen for its ability to classify data with high accuracy and its fast and efficient computational process. The results of this study indicate that out of 5 test data, 4 results matched the predictions, while 1 result did not. These findings demonstrate that the system can provide vehicle recommendations to online drivers with an accuracy rate of 80%, thereby assisting prospective drivers in making well-informed decisions.]]>
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