<![CDATA[Acute Respiratory Infection (ARI) remains a major public health concern in Indonesia, including at the Ciputat Community Health Center, which handles a high volume of patients on a daily basis. Limited consultation time and low public health literacy among the community have caused early identification of ARI symptoms to often be delayed or inaccurate. This study aims to design and implement a web-based expert system for early ARI diagnosis using the Certainty Factor (CF) method. The system processes user-selected symptoms along with their associated belief degrees to handle uncertainty during the reasoning process. Knowledge base parameters, including Measure of Belief (MB) and Measure of Disbelief (MD), were obtained through consultation with medical experts. System testing was conducted using three methods, namely Black Box Testing, White Box Testing with Cyclomatic Complexity analysis, and User Response Questionnaires distributed to 37 respondents. The Black Box test confirmed that all functional features of the system operate correctly and as expected. The White Box test yielded a Cyclomatic Complexity value of 2 across all core modules, indicating logical structural simplicity and correctness of the program flow. Furthermore, the User Response test achieved a satisfaction score of 89,12%, which falls into the "Very Good" category. The developed system effectively assists users in performing preliminary self-identification of ARI symptoms and supports healthcare personnel in streamlining the triage process before formal medical consultation.]]>
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