<![CDATA[Dengue Hemorrhagic Fever (DHF) remains a major public health burden in tropical and subtropical countries, with Indonesia consistently reporting the highest incidence in Southeast Asia since 1968. Early diagnosis traditionally depends on clinical evaluation and laboratory confirmation, processes that may require several days and often delay intervention during the critical plasma leakage phase. Addressing this gap, the present study introduces an intelligent early identification system for DHF based on the Naïve Bayes Classifier, a probabilistic data-mining method recognized for its computational efficiency and strong performance in handling categorical medical attributes. The model was trained using 100 anonymized patient records and DHF screening forms collected from Puskesmas Pasir Buah, Curug, Bitung, spanning 2020–2023, incorporating twelve clinically relevant predictors consisting of symptom-based indicators and basic hematological parameters. Following preprocessing and 10-fold cross-validation, the system achieved an average accuracy of 94.67%, precision of 95.2%, recall of 93.8%, and an F1-score of 94.5%, demonstrating its reliability for preliminary DHF assessment. The resulting web-based prototype allows health workers to input patient symptoms and receive immediate probabilistic classifications (Positive/Negative) accompanied by recommendations for confirmatory laboratory testing. By providing rapid and interpretable diagnostic support, this system has the potential to reduce diagnostic delays at the primary healthcare level and enhance decision-making in resource-limited environments.]]>
