<![CDATA[Acute Respiratory Infection (ARI) has a high prevalence in Indonesia, but the manual diagnosis process faces challenges such as limited medical personnel and uncertainty in symptom analysis. This study developed and compared two AI methods, namely Naïve Bayes and Dempster-Shafer, in a web-based expert system to diagnose ARI. Symptom and disease data were collected from literature and experts, then implemented in a PHP and MySQL-based system. Naïve Bayes was used for probability-based classification, while Dempster-Shafer handled uncertainty. Testing was conducted on one case of ARI. Naïve Bayes produced a probability of 21.99% for Pneumonia, while Dempster-Shafer provided a combined probability of 61.6% for five diseases, including Colds, Acute Pharyngitis, and Epiglottitis. The results show that Naïve Bayes is suitable for consistent single diagnoses, while Dempster-Shafer is more appropriate for conditions with overlapping symptoms and uncertain data]]>
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