<![CDATA[Rapid advancements in information technology have driven significant transformations across various sectors, including the automotive industry. One of the challenges faced by motorcycle repair shops is the diagnostic process, which is still performed manually and relies on the technician’s intuition, often leading to inefficiencies and potential errors in identifying issues. This study aims to develop an expert system based on the forward chaining method to facilitate a faster and more accurate initial diagnosis of motorcycle malfunctions. A case study was conducted at AKC Workshop, which faced similar challenges. This expert system was developed using a rule-based approach to mimic an expert’s thought process in diagnosing faults. Based on the test results, when the motorcycle exhibited symptoms G9, G10, G11, and G16, the system successfully identified the fault with a 100% match rate on rule 2 (R2), indicating that the motorcycle had a battery issue (weak battery). These results demonstrate that the developed expert system is capable of providing accurate diagnoses and has the potential to improve the efficiency and accuracy of services at motorcycle repair shops]]>
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