<![CDATA[Recently, Indonesian online news media have frequently reported cases of repair shops providing inaccurate motorcycle damage diagnoses to gain higher profits. This practice disadvantages users, especially those with limited technical knowledge of their vehicles. Such diagnostic errors often lead to repair costs that do not align with the actual severity of the damage. To address this issue, a motorcycle damage diagnosis application based on the Fuzzy Tsukamoto method was developed, allowing users to identify their vehicle's condition based on observed symptoms. The Fuzzy Tsukamoto method was selected for its ability to handle data uncertainty and generate specific damage severity levels. The system receives symptoms as input from the user, which are then processed through fuzzy rules to determine the type and severity of the malfunction. This application was developed using Flutter for the user interface and Laravel as the backend for data processing. Through this application, it is expected that users can obtain an initial overview of their motorcycle's damage, enabling them to make more informed and accurate decisions.]]>
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