<![CDATA[Academic stress is a common problem experienced by students due to high academic demands, which impacts academic achievement and mental health. Therefore, an early detection mechanism is needed to help identify the level of academic stress quickly and objectively as a basis for providing appropriate counseling services. This study aims to develop a Certainty Factor-Based Expert System to identify students' academic stress levels and provide counseling recommendations based on the diagnosis results. The Certainty Factor method is implemented by combining the Expert Certainty Factor (Expert CF), User Certainty Factor (User CF), and Rule Certainty Factor (Rule CF) to accommodate uncertainty in the inference process, while the knowledge base is compiled through knowledge acquisition from guidance and counseling experts based on the Perception of Academic Stress (PAS) indicator. The results show that the system is able to identify academic stress levels based on the symptoms selected by students, generate a Certainty Factor value as a level of confidence in the diagnosis, and provide counseling recommendations that are appropriate to the identified stress categories. In the test scenario, the system generated a Certainty Factor value of 77.6%, indicating a Moderate Stress category (S2). This research contributes to the development of an expert system that integrates the identification of academic stress levels and counseling recommendations in one web-based application so that it can support the early detection process and decision-making for students, academic advisors, and counselors.]]>
