Selamat Datang Di Repository Universitas Royal

The increasingly complex electrical systems in motorcycles, driven by technological advancements, make manual diagnosis difficult, especially for novice technicians. At Bengkel Ari, located in Kisaran Timur, Kabupaten Asahan, common electrical issues such as dead batteries, non-functioning lights, and faulty regulators are frequently encountered. This study aims to develop an expert system to assist mechanics in identifying electrical problems quickly and accurately. The method used is the Bayes method, which allows the system to calculate the probability of a specific fault based on the symptoms provided, thus offering relevant diagnoses. Technical knowledge was gathered from experienced mechanics and structured into a knowledge base for the system. The results of this research are expected to improve repair efficiency, reduce dependence on senior technicians, and serve as a reference for future development of expert systems in the automotive field.
Keywords: Expert System, Fault Diagnosis, Motorcycle, Technician, Bayes Method