COVID-19 DIAGNOSIS EXPERT SYSTEM WITH CERTAINTY FACTOR METHOD
Abstract
Covid-19 is a new type of virus that is currently a pandemic in almost all countries. The limitations of experts for diagnosis and recommending rapid action to patients who are diagnosed positively, as well as the limitations of the expert area in the environment wh ere the Covid-19 outbreak occurred. This expert system aims as an early diagnosis system for Covid-19 so that patients can receive treatment immediately, and help stop the wider spread of the virus. Using predetermined parameters and there is also a system that can later make it easier for users to find out the diagnosis of Covid-19 caused by the coronavirus. The method used for this expert system research is Certainty Factor (CF). This method aims to showed how much the confidence value is based on the clinical parameters given by MYCI. The advantage of the Certainty Factor method is that it can be measured something that is certain or uncertain. And to maintain accuracy, Certainty Factor can only process two data. The Expert System uses the Certainty Factor (CF) method to diagnose Covid-19 disease, starting with the collection of symptom data and giving confidence values by the expert on each symptom, then the symptom data input stage from the user and the system will calculate based on the Certainty Factor rule, multiplying the weight value namely user CF and expert CF, combining the results of the multiplication of each symptom, so that a percentage of confidence is obtained as the final diagnosis result. After calculating the accuracy test of the 3 rule system with 99% positive Covid-19 results, 48% most likely Covid-19 and 29% negative Covid-19.
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