Diagnosis of Ear, Nose, and Throat (ENT) diseases often faces obstacles in determining the level of certainty of a disease based on the symptoms experienced by the patient. The main problem in this research is how to compare the level of accuracy between the Certainty Factor and Dempster-Shafer methods in an expert system for diagnosing ENT diseases. As a solution, this research applies both methods and analyzes the results of their calculations on various symptoms entered by the patient. The purpose of this research is to determine which method is more effective in providing certainty of diagnosis. The results show that the Certainty Factor method produces a higher level of certainty than Dempster-Shafer, for example in Tonsillitis disease which reaches 94.68% compared to 0.02% in Dempster-Shafer. Thus, the Certainty Factor method is more recommended for ENT disease diagnosis expert systems. The contribution of this research is to provide insight into the use of artificial intelligence methods in the medical field, especially in improving the accuracy of expert systems to assist health workers in making diagnostic decisions.