<![CDATA[Dengue fever remains a serious public health issue in tropical regions such as Indonesia, with the Belian District in Batam experiencing periodic outbreaks that demand effective early detection systems. This research aims to design and implement a web-based intelligent system that utilizes the Naive Bayes classification method to detect potential dengue fever cases based on user-input symptoms. The system was developed using web technologies (HTML, CSS, JavaScript, PHP/Python, and MySQL) and allows users—both medical personnel and the general public—to input symptoms and receive real-time probabilistic predictions. The dataset used in this study consisted of 200 records of patient medical history, divided into training and testing sets. Performance evaluation of the model using accuracy, precision, recall, and F1-score yielded promising results: 87% accuracy, 85% precision, 89% recall, and 87% F1-score. The system also features data management modules for symptoms, diseases, rules, users, and consultation history, supporting structured diagnostic processes. The results show that the Naive Bayes method effectively supports intelligent, data-driven decision-making for early dengue detection and can serve as a valuable screening tool, especially in areas with limited medical resources.]]>
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