System Implementation Using Certainty Factor Method For Early Diagnosing Corona Virus Disease (COVID-19)

Abstract

Humans are facing a non-natural disaster that threatens the entire human population on Earth. Non-natural disaster  is called Corona Virus Desease (COVID-19), which is a large family of viruses that can attack humans and animals that are currently a global pandemic. Humans usually cause respiratory infections, ranging from the common cold to serious illnesses  such as MERS  and SARS. COVID-19  itself  is  a new  type of  coronavirus  found  in humans  and  in  the Wuhan area, Hubei  Province,  China  in  2019.  To  assist medical  staff  in  early  detecting  symptoms  experienced  by  patients  and  facilitate  the administration of hospital records, one of them was made an expert system  that could detect this COVID-19 early with the Certainty Factor (CF) method. This expert system mimics similar symptoms experienced by COVID-19 patients and will be grouped  into  several  patient  statuses.  Patients  who  experience  serious  symptoms  will  be  grouped  into  Patients  Under  Supervision  (PDP) and patients who are considered  to have milder symptoms will be grouped into  Insider Oversight status (ODP) while those who experience symptoms that are outside of the main symptoms will be classified into Non Suspect (NON) status. From 152 patient data inputted in this study, 114 ODP results with an average CF value of 91.38%, 36 PDP with an average CF value of 98.25% and 2 NONs with an average CF value of 40%. CF with system calculation experiments that represent patients get a CF value of 0.998848 or 99.88% to PDP. This expert system can be used to make decisions that can help medical personnel perform  actions  and  administer better before conducting a  through  test  in  the  laboratory  to  ensure positive or negative patients COVID-19.  .