<![CDATA[This study focuses on developing an expert system for diagnosing skin diseases using the Certainty Factor and Forward Chaining methods. The increasing prevalence of skin diseases and the similarity in symptoms among different conditions make accurate initial diagnoses challenging without expert help. Expert systems are valuable in supporting early medical decisions and improving public access to diagnostic information. The study aims to diagnose skin diseases based on symptoms while considering the uncertainty in medical decision-making. The novelty of the study lies in integrating the Certainty Factor method to quantify confidence in diagnoses with the Forward Chaining inference mechanism, which processes symptom-based rules. A rule-based expert system approach is used, with data on symptoms and diseases gathered from literature and expert knowledge. Forward Chaining serves as the inference engine, while the Certainty Factor method calculates the certainty level of diagnoses based on user-selected symptoms. The results show that the expert system can accurately diagnose skin diseases and provide a certainty value aligned with expert opinions. The combination of Certainty Factor and Forward Chaining enhances the clarity and reliability of the diagnosis. The study concludes that the proposed system effectively supports the initial diagnosis of skin diseases by providing diagnostic results along with a certainty level.]]>
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