The Certainty Factor Method in An Expert System for Tuberculosis Disease Diagnosis - Dimas Maulana Dwi Kumara; Linda Perdana Wanti; Riyadi Purwanto (Bulletin of Computer Science Research, 2025)
Tuberculosis is an infection caused by acid-fast bacilli (AFB) and is an infectious disease that can attack anyone through the air. This disease is hazardous and chronic, with a high prevalence among individuals aged 15-35 years. The diagnosis of tuberculosis traditionally takes a long time because it involves an interview process by medical experts and testing sputum samples in the laboratory to determine whether the patient is positive or negative for this disease. This process is not only time-consuming but also requires significant resources. To overcome this problem and speed up the diagnosis process, a technology-based approach is needed, namely the Expert System with the certainty factor method. This method can handle uncertainty in medical diagnosis by providing a certainty value for each observed symptom. This article discusses in depth the application of the certainty factor method in an expert system to diagnose Tuberculosis. By using this method, the system can provide faster and more accurate diagnosis results in diagnosing tuberculosis with a confidence level of 94.6% and reduce the workload of medical personnel. The application of the certainty factor method allows the integration of various symptoms and relevant medical data to produce more precise and reliable diagnostic conclusions.