Currently, the world is facing a global pandemic that attacks all countries. In Indonesia, there are three types of status for suspected patients: asymptomatic person, Person Under Supervision, and Patient Under Supervision. The statuses are issued by a paramedic, conducting medical examinations or direct interviews with patients with several criteria. We conducted several non-medical experiments to assist medical personnel in determining the asymptomatic. We exploit the case-based reasoning (CBR) for determining the suspected patients, and the K-NN (K-Nearest Neighbor) for data grouping based on the level of similarity. The patients will be interviewed regarding their travel history, direct contact history, health status, and some other information for the past 14 days. This combination delivers the information of the similarity level from the given data and previous data. As a conclusion, the percentage level of similarity can be used by a medical officer to issue the status of patients and giving several recommendations to follow health protocols.
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