Expert System for Diagnosing Monkeypox Using the Tsukamoto Method and Forward Chaining - Marwati, Fingki; Ardianto (LPPM Politeknik Aceh Selatan, 2026)
Monkeypox is a viral infectious disease that requires early detection to prevent wider transmission and ensure appropriate treatment. Limited public awareness and access to medical professionals may delay early diagnosis. Therefore, this study proposes the development of an Android-based expert system for early monkeypox diagnosis using the Forward Chaining inference method and the Tsukamoto fuzzy logic method. Forward Chaining is applied to perform rule-based reasoning based on user-input symptoms, while the Tsukamoto method is used to calculate the level of certainty of the diagnosis. The system was developed using the Waterfall model and tested with 20 case data samples. The evaluation results show that the system achieved an accuracy level of 85%, with 17 out of 20 diagnoses consistent with expert assessments. User testing involving 20 participants indicated that 90% of users found the application easy to use and informative. In addition, the system is capable of generating diagnostic results within 1–2 minutes, making it more efficient than manual consultation. The results demonstrate that the proposed system is effective and feasible as a decision-support tool for early monkeypox diagnosis.