Forward Chaining and Certainly Factor Method Optimization for Lung Disease Expert System

The purpose of this research is to build a system which is capable of diagnosing lung disease, to provide information about lung disease along with treatment solutions so that it can help the public, and to determine the best method to use in the system to be built. From the results of the analysis by comparing several methods that can be used in Expert Systems for diagnosing lung disease, the result is that by implementing a combination of the Forward Chaining method and the Certain Factor, the highest level of accuracy of results is obtained among other methods. After several tests using software testing, user questionnaires and expert validation, it is proved that this Expert System can assist patients in diagnosing lung disease with a diagnostic accuracy rate of 93 percent, and ready used by the public.