<![CDATA[This research aims to develop an expert system for detecting coronary heart disease by comparing the Demster-Shafer and Certainty Factor methods in providing accurate solutions. Coronary heart disease (CHD) is a disease that often threatens human health. To overcome this problem, the development of expert systems has become an important approach in diagnosing CHD accurately and efficiently. The problems faced include the level of complexity in diagnosing CHD and the need for solutions that can provide a high level of confidence. The method used involves collecting data from various sources and analysis using both methods to determine a diagnosis. The research results show that both methods are able to provide satisfactory results, however, a comparison between the two provides additional insight in understanding the reliability and accuracy of the expert system being developed. A thorough analysis shows that the Demster-Shafer method provides a higher degree of accuracy in some cases, while Certainty Factor tends to provide faster results. However, this research also reveals that optimal results can be achieved by combining the two methods. Thus, this research makes an important contribution to the development of an expert system for coronary heart disease detection and provides a foundation for further development in this domain. In conclusion, the integration of the Demster-Shafer and Certainty Factor methods shows the potential to improve the performance and reliability of expert systems in supporting CHD diagnosis effectively. The calculation results of both methods show that the Dempster-Shafer Method produces a certainty level of 99.8%, while the Certainty Factor Method provides a confidence level of 92%.]]>
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