Comparative Analysis of Certainty Factor Method and Bayes Probability Method on ENT Disease Expert System - Setyaputri, Khairina Eka; Fadlil, Abdul; Sunardi, Sunardi (Scientific Journal of Informatics, 2018)
Expert system is computer programs that mimic the thought process and expert knowledge in solving a particular problem. Basically, an expert system has various methods to diagnose various kinds of diseases experienced by humans, animals, and plants. This research analyzes the comparison of Certainty Factor method and Bayes Probability method in the expert system of Ear, Nose, and Throat (ENT) diseases. Both methods have the same basic theory of overcoming uncertainties with existing variables. The Certainty Factor method has many variables that are used as systematic knowledge, namely the weight value of the expert which is the basis of knowledge of the system and the user input weight value, while the Bayes Probability method uses only expert knowledge in the calculation. Based on a comparative analysis of the methods obtained with 10 patients data on the ENT disease expert system, the Certainty Factor method has accuracy in diagnosing the disease by 100%, while the Probability Bayes method of system accuracy is 80%. So it can be concluded that the Certainty Factor method is more accurate in diagnosing ENT than the Bayes Probability method.