<![CDATA[This study aims to develop a web-based expert system for diagnosing electric bicycle faults using the backward chaining method. It addresses the limitation of previous systems that did not support user input of fault hypotheses. The research stages include literature review, data collection (31 faults and 5 symptoms), implementation of web-based inference, and black box testing. The results demonstrate that the system successfully accommodates user-input hypotheses and related symptoms, then matches them with rules to generate diagnoses. Functional testing confirms all features operate as intended. The research novelty lies in: (1) the first comprehensive knowledge base for electric bicycles (31 faults), (2) an interactive web interface supporting hypothesis input, and (3) dynamic database storage for rule updates.]]>
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