Classification of Anxiety Levels Entering the World of Work in Final Year Students Using the Neighbor Weighted K-Nearest Neighbor Method

Pratomo, Awang Hendrianto
Adam, Muhammad Fahmi
Prasetyo, Dessyanto Boedi

Knowledge Engineering and Data Science

This study evaluates the accuracy of the Neighbor Weighted K-Nearest Neighbor (NWKNN) method in classifying the anxiety levels of final-year students as they prepare to enter the workforce, particularly in cases of unbalanced data distribution. The system was developed using the prototype method, and NWKNN was applied to classify anxiety levels into low, medium, and high categories. Testing using the Confusion Matrix demonstrated strong performance, achieving an accuracy of 94% based on a dataset of 1009 students, with a 90:10 ratio of training to test data. The results indicate that NWKNN effectively provides classification input values, making it a reliable tool for early detection of work-related anxiety. By identifying students with high anxiety levels, this approach can support targeted interventions to help students manage stress, improve self-confidence, and enhance their preparedness for the job market. The application of NWKNN in mental health classification thus holds significant potential in assisting universities and career counseling services in developing proactive strategies to mitigate anxiety and support students’ psychological well-being.