Development of a Web-Based Expert System of Human Eye Disease Diagnosis Based on Symptoms Using the Certainty Factor Method - Dionata Pin Asa; Agus Sidiq Purnomo (Poltanesa, 2026)
Limited access to eye health services in Indonesia, the scarcity of ophthalmologists, and low public health literacy regarding early symptoms of eye diseases are major problems that drive the need for technology-based solutions. This study aims to develop a web-based expert system for diagnosing human eye diseases based on symptoms using the Certainty Factor method. The research methodology uses Turban's decision-making model consisting of four phases: intelligence, design, choice, and implementation. The intelligence phase includes observation, literature study, and interviews with eye experts to obtain data on six types of eye diseases (Low Vision, Cataract, Conjunctivitis, Glaucoma, Keratitis, Blepharitis) along with 16 clinical symptoms and expert CF values. The design phase includes knowledge base design, system flow, database, and user interface. The choice phase performs manual and system CF calculations. The implementation phase builds a web-based system using PHP and MySQL. Testing results on 40 patient data showed that the system achieved 100% accuracy according to expert diagnosis. A total of 77.5% of patients had confidence levels in the High and Very High categories. Manual and system calculations showed identical results in all cases. In conclusion, the Certainty Factor method is effective and reliable for web-based eye disease diagnosis.