Self-checking Corner of Upper Acute Respiratory Infection Disease Using Certainty Factor Method

The Covid-19 pandemic has altered people’s behaviors, particularly those of students. Students prefer health consultations through telemedicine applications, which in 2020 recorded a surge of up to 600%. Telemedicine applications require costs incurred and inefficient time to conduct consultations. Seeing this, it is necessary to have a health-checking application at an institution, especially a campus, which is free of charge, without direct contact and has valid information, and is supported by artificial intelligence. The solution offered in this research provides a Self-Checking Corner application called Self-Heal as a pre-diagnosis of upper Acute Respiratory Infection (ARI) using the expert system method, Certainty Factor. This paper proposes the creation of an application based on the certainty factor method with valid data from experts. It optimizes the categorization and weighting of scalars, so it can be used for pre- diagnosis before the user consults a doctor. The use of data on symptoms, diseases, and rule tables is obtained from experts, namely doctors so that data validation is more accurate. The accuracy of the initial diagnosis of Acute Respiratory Infection (ARI) disease is close to 80% when the doctor diagnoses directly from 3 trials conducted. The application can be as an application for pre-diagnosis of upper respiratory tract diseases that save time and without cost.