<![CDATA[This study aims to analyze and design an expert system for diagnosing hernia using the Forward Chaining and Certainty Factor methods. This system was developed to assist in the process of determining a diagnosis based on the main symptoms experienced by the patient, while also providing appropriate treatment recommendations. To address the problem of knowledge uncertainty that often arises in expert systems, this study integrates the Certainty Factor method to measure the level of confidence in the diagnosis results. Meanwhile, the Forward Chaining method is used as a reasoning mechanism that starts from facts or symptoms provided by the patient to a conclusion in the form of a disease diagnosis. The diagnosis process in this system begins with a consultation session, where the system will ask a series of relevant questions according to the symptoms experienced by the patient. Based on the answers given, the system will make inferences to produce a diagnostic decision. Based on the test results, the expert system built is able to provide a diagnosis that is close to the assessment of medical experts. The Certainty Factor testing model provides advantages in measuring the level of confidence in the diagnosis, so that the results given are not absolute, but have a more realistic probabilistic value. Thus, an expert system for diagnosing hernia is able to overcome uncertainty and produce a confidence level value for the diagnosis. Based on test results, the system demonstrated a fairly good accuracy rate, around 90%–97%, depending on the combination of symptoms selected. Thus, the CF method is effective as an aid in initial diagnosis, although it still requires further examination by medical personnel.]]>
