<![CDATA[The rapid growth in the use of electric motorcycles has increased the need for a damage diagnosis system that is fast, accurate, and easily accessible. The process of identifying faults, which still relies on manual inspection, often requires considerable time and depends heavily on the expertise of technicians. This study aims to develop a web-based intelligent system capable of automatically diagnosing electric motorcycle faults using the Naïve Bayes method. This method is chosen due to its ability to perform probabilistic classification with good accuracy, even when dealing with limited data. The developed system utilizes symptom data and types of faults as the basis for probability calculations to determine the most likely damage. The system development process includes data collection, model design, algorithm implementation, and system testing. The testing results indicate that the system is able to provide fast and consistent diagnostic recommendations based on the symptoms input by users. With the implementation of this system, it is expected to assist both users and technicians in conducting initial identification of electric motorcycle faults more efficiently, thereby accelerating the repair process and reducing the potential for diagnostic errors.]]>
Copyrights © 2026
