Diagnosis of Tuberculosis by Using a Fuzzy Logic Expert System

Tuberculosis (TB) is one of the diseases that causes high mortality in humans. The prevention of this disease has been sought by medical professionals and researchers. Unfortunately, the handling of TB is still manual and very dependent on medical experts who are very limited in number. In this study we propose an alternative information technology to overcome this problem. To overcome this problem a TB diagnostic system is developed using a fuzzy expert system. Input data on this system are the symptoms suffered by the sufferer, which consists of cough, weight loss, breathless, loss of appetite, and fever. The input data is then processed using fuzzy logic which consists of a process of fuzification, inference and defuzification. The output of the system displays the disease diagnosis interface on the web. Disease rules are given by experts who are experts in their fields and from journal sources. The results of the study are information systems that can provide the results of disease diagnosis to the user. The calculation of the accuracy value is also done to find out how accurate the fuzzy logic is in this system, and from the results of these calculations it is found that the accuracy value is 90% which shows that fuzzy logic is good for the diagnostic process.