Expert System for Diagnosing Autoimmune Diseases Using Dempster–Shafer and Fuzzy Logic: A Case Study of Prof. Dr. Margono Soekarjo Regional Hospital

Rahmadani, Ragil Putri
Nur, Yohani Setiya Rafika
Utami, Annisaa

Jurnal Teknik Informatika (JUTIF)
Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026

Autoimmune diseases, particularly lupus, pose a major challenge in healthcare because their symptoms are highly variable and often mimic other medical conditions. Delayed diagnosis can worsen patient outcomes, increase the risk of severe complications, and even lead to death, especially in healthcare facilities with limited autoimmune subspecialists, such as Prof. Dr. Margono Soekarjo Regional Hospital. This study aims to develop a web-based expert system to support early screening for lupus by combining the Fuzzy Tsukamoto method and the Dempster-Shafer theory. The Fuzzy Tsukamoto method is used to represent symptom uncertainty through fuzzification, while the Dempster-Shafer theory is used to combine evidence from individual symptoms to produce confidence levels for possible diagnoses. The research process included a literature review, expert interviews, construction of a symptom–disease knowledge base, design of fuzzy rules, implementation of mass function calculations, and development of a web-based diagnostic application. Testing was conducted using ten patient test cases with confirmed expert diagnoses. The test results showed an accuracy of 100%, with all system diagnoses matching the experts’ diagnoses. The strength of this research lies in the integration of two inference methods to improve the accuracy of evidence calculation, and in the use of symptom uniqueness and occurrence parameters that were validated directly by experts. This system has the potential to serve as an effective early screening tool for healthcare providers and patients, particularly in resource-limited settings. From an informatics perspective, this study contributes to the development of intelligent decision support systems by demonstrating the effectiveness of a hybrid reasoning approach in handling uncertainty in medical diagnosis. The integration of Fuzzy Tsukamoto and Dempster–Shafer methods enhances diagnostic consistency and reliability, making the proposed system relevant for research in expert systems and medical informatics.