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All Journal Elimensi Journal of Electrical Engineering
Sastika Yolanda 12
Universitas Pembangunan Panca Budi
Automotive Engineering Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering

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Design of Web-Based Dengue Disease Detection Intelligent System by Applying Naive Bayes Method in Belian District Batam Sastika Yolanda 12; Rahmadani; Muhammad Iqbal
Journal of Electrical Engineering Vol. 4 No. 01 (2026): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v4i01.684

Abstract

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.
Co-Authors Muhammad Iqbal Rahmadani