A Mobile-Based Expert System for Glaucoma Diagnosis Using the Naive Bayes Algorithm - Amelia Bahar; Fajar Novriansyah Yasir; Sukmawati Sukmawati (Yayasan Cita Cendekiawan Al Khwarizmi, 2025)
This study aims to develop a mobile-based expert system application for early diagnosis of glaucoma using the Naive Bayes algorithm. The application is designed to help users recognize early symptoms of glaucoma, provide preliminary information, and increase public awareness to reduce the risk of vision loss or blindness. The application was developed using the Dart programming language, the Flutter framework, and Firebase as the database platform. The research method employed is Research and Development (R&D), utilizing the 4D development model, which consists of four stages: Define, Design, Develop, and Dissemination. To evaluate the functionality and effectiveness of the application, both black-box testing and expert validation were conducted. The Naive Bayes algorithm implemented in the application demonstrated a high accuracy rate of 97.50%, indicating strong reliability in recognizing symptom patterns and producing appropriate diagnostic predictions based on user input. Furthermore, the System Usability Scale (SUS) was used to assess the application's usability, yielding a high average score of 97.5%, reflecting excellent ease of use and user satisfaction. In addition, content validation by subject matter experts resulted in an average feasibility score of 98.07%, indicating that the application is highly suitable for public use in supporting early screening and diagnosis of glaucoma.