Student health directly affects the quality of learning and academic achievement. When sleep patterns and daily routines become irregular, students face a higher probability of developing health-related problems. To address this issue, a web-based expert system was designed and developed to detect student health risks based on sleep patterns and daily activities, applying the Certainty Factor (CF) method, with a case study conducted at STIE BPKP. The Certainty Factor method was used to quantify the degree of confidence in a particular health risk by combining symptom data selected by users with certainty weights assigned by domain experts. The system was built as a web application featuring three core functions: symptom data input, diagnostic processing, and output presentation in the form of health risk levels along with actionable recommendations. Functional testing was carried out to verify that each module operated correctly and that diagnostic outputs aligned with the expected Certainty Factor calculations. Findings from this study confirm that the expert system successfully assists students in assessing their health risk levels in a fast and accessible manner. Beyond individual use, the system also serves as a supporting instrument for academic institutions seeking to strengthen health awareness programs among their student population. As a result, this expert system holds practical value as an early-warning tool for student health risk detection.