ABSTRACT
COVID-19 is a disease caused by the SARS-CoV-2 virus or often referred to as Corona Virus. In December 2019, this virus begin to spread from Wuhan, China to all over the world and was declared a pandemic. The virus attacks the respiratory tract so that sufferers have symptoms such as acute respiratory infection. In many cases, there are also patients with COVID-19 who do not have the following symptoms, making it difficult to determine the patient's COVID-19 status before a PCR test is performed. In this research, we try to do a rapid diagnosis with the final status of COVID-19 such as close contact, suspect, probable, and confirm, based on symptoms experienced by patients using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. ANFIS was chosen because ANFIS uses an artificial neural network concept that is suitable for use in patterned and complex calculations. ANFIS also has the basis of fuzzy logic that can map the expert and linguistic aspects of humans. Generated model from ANFIS training tested with entering symptoms patients data, then matched with COVID-19 status. Error calculation using MAE as an evaluation of the accuracy of this model. Evaluation is based on 10-fold cross validation. The experimental results obtained an accuracy of 82.39% with an MAE value of 0.1558 for training and 0.1903 for testing. © 2022 IEEE.