Deteksi Stres Mahasiswa Berdasarkan Komentar Media Sosial X Menggunakan TF-IDF dan Algoritma Logistic Regression - Alvin Rama Saputra Alvin; Muhammad Wifaqul Azmi; Anggraini Puspita Sari (JOINS (Journal of Information System), 2026)
Mental health issues, particularly stress among university students, are on the rise and require special attention. Students tend to express their psychological conditions implicitly through comments or posts on social media, especially on platform X, which provides valuable digital data for real-time and non-invasive emotional analysis. This study aims to develop a stress detection system for students by analyzing comments on social media platform X using the Term Frequency-Inverse Document Frequency (TF-IDF) method and the Logistic Regression algorithm. TF-IDF is applied to extract important linguistic features from student comments, while Logistic Regression is chosen for its ability to provide clear probabilistic interpretation and efficiency in processing high-dimensional text data. The model is trained using labeled student comment data and evaluated using accuracy, F1-score, precision, and recall metrics. The results indicate that the system developed can classify stress and non-stress comments with a high accuracy of 93%, demonstrating great potential in supporting early interventions for student mental health. The implication of this research is expected to serve as a foundation for the development of digital applications that are responsive, adaptive, and practical in promoting student mental well-being in Indonesia.