Implementation of the Forward Chaining Algorithm in a Student Mental Health Detection System
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Student mental health is an important aspect in supporting academic success and individual well-being. The various academic pressures, social challenges, and the transition to independent living make students a vulnerable group to mental health disorders such as stress, anxiety, and depression. However, many students are still reluctant or experience difficulties in accessing professional services to solve this problem. This research aims to develop an early detection system for student mental health based on an expert system using Forward Chaining. This method performs reasoning from symptoms toward a conclusion based on mental health condition using rules stored in the knowledge base. The system is developed using the DASS-21 (Depression, Anxiety, Stress Scale-21) instrument to assist in identifying mental health conditions. The dataset consists of 50 student respondents from Universitas Muhammadiyah Ponorogo who completed the DASS-21 questionnaire. System performance was evaluated by comparing the diagnostic outputs of the system against the standard DASS-21 score. The results were analyzed using a confusion matrix to calculate accuracy, precision, recall, and F1-score per severity class. The research results show that the system is capable of initially identifying students’ mental health conditions by presenting the severity level of the mental condition, a description of the condition, and appropriate handling recommendations. Black-box testing confirmed the accuracy of 96%, with precision and recall values above 90% across all severity classes. These results demonstrate that the implemented forward chaining system provides an accessible, automated, and standardized tool for early mental health detection in the Indonesian higher education context.
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