Expert system for motorcycle damage diagnosis using certainty factor and case-based reasoning methods

Abstract

Motorcycles are the main mode of transportation that requires maintenance due to damage. Unfortunately, damage detection on motorcycles remains traditional, despite the potential for expert systems and artificial intelligence (AI) to facilitate more straightforward diagnosis for both motorcycle users and mechanics. That is why this research aims to develop a system with an expert system approach to diagnose damage to a motorcycle. This research applies the certainty factor (CF) expert system approach and the case-based reasoning (CBR) AI method. The study found that the expert system using the CBR method outperforms the CF method in diagnosing motorcycle damage. The CF expert system method achieves an accuracy performance of 98%. In comparison, the CBR expert system method achieves an accuracy of up to 100%, or can be equated with human mechanics in diagnosing motorcycle damage. The finding implies that CBR can effectively replace human experts in detecting motorcycle damage.