A Naïve Bayes Approach to Understanding Students’ Pychological Well-Being : Pendekatan Naïve Bayes untuk Memahami Kesejahteraan Psikologis Siswa - M. Riko Anshori Prasetya; Febria Sera Darnefi (INSTALL: Information System and Technology Journal, 2024)
High levels of stress and depression among Indonesian students are significant issues that demand special attention. According to data from the National Crime Information Center (Pusiknas) of the Indonesian National Police (Polri), there were 971 cases of suicide related to the mental health of students from January to October 18, 2023. This study utilizes an open dataset from Kaggle, which includes information on students' mental health conditions, and applies the Naive Bayes algorithm to determine accuracy, precision, and recall values. The Naive Bayes approach is employed to classify the mental health status of students and identify those who require special assistance. The results indicate that the Naive Bayes algorithm is effective in identifying students needing special help, with a high accuracy rate. Testing with RapidMiner yielded an accuracy of 90.00%, a recall rate of 89.66% for the 'No' class and 100.00% for the 'Yes' class. Precision for the 'No' label was 100.00%, while for the 'Yes' label, it was 25.00%. This approach can aid campuses, families, and professionals in identifying students who need special attention.