<![CDATA[Acute respiratory tract infection is a health problem that often occurs and requires a fast and accurate diagnosis to prevent further complications. The high incidence of ARI raises the need for a more accurate and faster diagnosis system. This research aims to analyze the application of fuzzy logic in an expert system so that it can diagnose acute respiratory infections (ARI) in Android-based applications. This expert system uses a fuzzy logic method to handle uncertainty and variability in the symptoms experienced by patients. The method used in this research involves collecting symptom data from patients, which is then processed using fuzzy rules to produce a diagnosis. This system is designed to provide easy access for users via Android devices by facilitating users in entering the symptoms experienced by the patient and providing an initial diagnosis along with the level of confidence in each diagnosis given so that the patient can carry out an initial examination independently before consulting with professional medical personnel. The research results show that the application of fuzzy logic to this expert system is able to provide fairly accurate diagnosis results, seen from the Deviation results. Patients suffering from mild acute respiratory infections with a final score of 4,804. Apart from that, this system also received a positive response from users because of its ease and speed in use. This research concludes that fuzzy logic is effectively applied to expert systems for the diagnosis of respiratory tract infections and has the potential to be further developed to improve health services in the community.]]>
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