KOMPARASI METODE CERTAINTY FACTOR DAN TEOREMA BAYES PADA IDENTIFIKASI KERUSAKAN MOTOR PCX 160 MATIC INJECTION BERBASIS ANDROID

Motorcycles have become one of the most mainstream forms of transportation. The majority of people need transportation to complete tasks every day, this is because motorcycles are more advantageous than other forms of personal transportation in terms of low maintenance and fuel costs. This is the background for designing a diagnosis and treatment expert system to utilize the signs that appear quickly and accurately to identify damage to the PCX 160 matic injection motorcycle. The methods used to diagnose matic injection motorcycle damage in expert systems are Certainty Factor and Bayes Theorem. The certainty factor method shows the expert's degree of confidence in the problem at hand as well as the accuracy of the facts. Bayes' theorem uses conditional probabilities as the basis for expressing the value of an event. The calculation results of both methods have similarities when predicting a damage well where both methods get accurasy and precision with the same percentage value of 92%.These two methods have similarities. When calculating the damage of an injected automatic motorcycle, both tend to obtain a large accuracy value based on the most indications. However, Certainty Factor is superior because it can predict more precisely with fewer indications, thanks to the confidence value that can be added by the user. Thus, in this study, the Certainty Factor method is superior to the Bayes' Theorem method.