Rancang Bangun Sistem Pakar Diagnosis Kerusakan Mesin Sepeda Motor dengan Pendekatan Supervised Learning Berbasis Mobile - Baskoro; Susilo Hartono; Agung Setio; Zahara Nabila (Sienna, 2026)
The increasing number of motorcycles in Indonesia is not accompanied by adequate technical understanding among their users, resulting in frequent delays in handling mechanical anomalies that result in fatal damage. This study aims to design and implement MotoDoctorAI, a mobile-based expert system that functions as a motorcycle damage diagnosis assistant in the Pringsewu Regency area. The research method uses qualitative and quantitative approaches through field observations at authorized repair shops, expert interviews for tacit knowledge acquisition, and an Agile system development model. This system integrates an inference engine to process engine, fuel system, and undercarriage damage symptoms into technical recommendations. Test results show that MotoDoctorAI successfully achieved a diagnostic accuracy rate of 85% when compared to expert mechanic verdicts. An additional feature in the form of a geographic information system for the location of the nearest repair shop in Pringsewu provides a practical contribution in accelerating access to after-sales service. This study concludes that digitizing expert knowledge into a mobile platform is effective in increasing vehicle maintenance independence for the general public, although further development is needed in the aspect of audio sensor integration to improve diagnostic precision.