Comparison of Machine Learning for Mental Health Identification (The DASS-21 Questionnaire)
Abstract
Stress appears at almost any age. Stress can disrupt mental and physical balance, and even students can experience it. Early detection of an individual's emotions is crucial. Researchers hope that by taking such actions, an individual can maintain self-control and prevent the stress they are experiencing from worsening. Bodily characteristics such as speech, body language, eye contact, and facial expressions indicate stress, depression, and anxiety. Psychological activities and human life are associated with physiological emotions. The three categories of negative thoughts or sad emotions are stress, anxiety, and depression. This research assesses or finds students who experience anxiety, depression, and stress. This study compares methods for determining mental health through the distribution of DASS-21 scale questionnaires. The researcher classified the research results using Naive Bayes, Decision Tree, k-NN, SVM, and Logistic Regression methods. According to experiments, SVM is effective for identifying mental health anxiety, depression, and stress with accuracy, recall, and precision of 0.86, 0.90, and 0.80. At Universitas Islam Lamongan, 138 engineering faculty students answered the DASS-21 questionnaire.
References
R. Rosdiana, A. Hasibuan, A. Chairani, M. Daud, and M. Sayuti, “Stress Diagnosis System Using Fuzzy Logic Method,” Motiv. J. Mech. Electr. Ind. Eng., vol. 4, no. 3, pp. 355–366, 2022, doi: 10.46574/motivection.v4i3.162.
P. Kumar, S. Garg, and A. Garg, “Assessment of Anxiety, Depression and Stress using Machine Learning Models,” in Procedia Computer Science, 2020, vol. 171, doi: 10.1016/j.procs.2020.04.213.
R. V. Shah et al., “Personalized machine learning of depressed mood using wearables,” Transl. Psychiatry, vol. 11, no. 1, 2021, doi: 10.1038/s41398-021-01445-0.
Y. H. Sun, H. Luo, and K. Lee, “A Novel Approach for Developing Efficient and Convenient Short Assessments to Approximate a Long Assessment,” Behav. Res. Methods, pp. 2802–2828, 2022, doi: 10.3758/s13428-021-01771-7.
A. Y. Mughal et al., “A systematic review of validated screening tools for anxiety disorders and PTSD in low to middle income countries,” BMC Psychiatry, vol. 20, no. 1. 2020, doi: 10.1186/s12888-020-02753-3.
R. Wardhani and N. Nafi’iyah, “Identification Of Mental Health Workers In Lamongan With Machine Learning,” J. Teknol. Inf. Univ. Lambung Mangkurat, vol. 8, no. 2, 2023, doi: 10.20527/jtiulm.v8i2.160.
R. Wardhani, N. Nafiiyah, and M. Kemal Farouq, “K-Means Clustering of the Mental Health of Engineering Students at the Universitas Islam Lamongan,” 2023, doi: 10.1109/COMNETSAT59769.2023.10420590.
A. Singh and D. Kumar, “Identification of Anxiety and Depression Using DASS-21 Questionnaire and Machine Learning,” Proc. 1st Int. Conf. Adv. Comput. Futur. Commun. Technol. ICACFCT 2021, pp. 69–74, 2021, doi: 10.1109/ICACFCT53978.2021.9837365.
J. Agarwal and J. Agarwal, “Detection and classification of anxiety students in university students through the application of machine learning,” Procedia Comput. Sci., vol. 218, no. 2022, pp. 1542–1550, 2023, doi: 10.1016/j.procs.2023.01.132.
S. M. Et. al., “Mental Health Prediction Models Using Machine Learning in Higher Education Institution,” Turkish J. Comput. Math. Educ., vol. 12, no. 5, 2021, doi: 10.17762/turcomat.v12i5.2181.
A. Priya, S. Garg, and N. P. Tigga, “Predicting Anxiety, Depression and Stress in Modern Life using Machine Learning Algorithms,” in Procedia Computer Science, 2020, vol. 167, doi: 10.1016/j.procs.2020.03.442.
S. R. Ahmad, N. Insani, and M. Salim, “Analysis of Cyberbullying on Social Media Using A Comparison of Naïve Bayes, Random Forest, and SVM Algorithms,” J. Teknol. Inf. dan Pendidik., vol. 17, no. 1, 2024, doi: 10.24036/jtip.v17i1.807.
A. S. Budi, P. H. Susilo, and N. Nafi’iyah, “SVM Algorithm for Predicting Rice Yields,” J. Teknol. Inf. dan Pendidik., vol. 13, no. 341, 2020.
M. Yuhendri, H. Hambali, and M. Muskhir, “Permanent Synchron Magnet Motor Speed Observer Based on Least Squares Support Vector Machine Regression,” J. Teknol. Inf. dan Pendidik., vol. 13, no. 2, 2020, doi: 10.24036/tip.v13i2.324.
I. Muluk, “Clustering Analysis of Internal and External Factors Affecting Post-Pandemic Study Duration in XYZ Educational Institution Using the Orange Application,” J. Teknol. Inf. dan Pendidik., vol. 16, no. 2, 2023, doi: 10.24036/jtip.v16i2.804.
F. A. Bachtiar, I. K. Syahputra, and S. A. Wicaksono, “Perbandingan Algoritme Machine Learning untuk Memprediksi Pengambil Matakuliah,” J. Teknol. Inf. dan Ilmu Komput., 2019, doi: 10.25126/jtiik.2019651755.
T. J. Melmambessy, “Analysis of the Opinion Students about The Online Learning System During the Pandemic Using The K-NN and Naïve Bayes Methods,” J. Teknol. Inf. dan Pendidik., vol. 16, no. 1, 2023, doi: 10.24036/jtip.v16i1.702.
S. Ahmad, N. Insani, and M. Salim, “Analysis of Cyberbullying on Social Media Using A Comparison of Naïve Bayes, Random Forest, and SVM Algorithms”, JTIP, vol. 17, no. 1, pp. 75-86, Jan. 2024, https://doi.org/10.24036/jtip.v17i1.807.
N. Ardelia, L. Lindawati, and N. Anugraha, “Comparison of Automation Deployment Implementation on Google Cloud Virtual Machine Using Deployment Manager and Terraform”, JTIP, vol. 17, no. 2, pp. 529-542, Jan. 2025, https://doi.org/10.24036/jtip.v17i2.869.
F. Kamarudin, F. Budiman, S. Winarno, and D. Kurniawan, “Optimizing Classification Algorithms Using Soft Voting: A Case Study on Soil Fertility Dataset”, JTIP, vol. 16, no. 2, pp. 255-268, Dec. 2023, https://doi.org/10.24036/jtip.v16i2.800.
Copyright (c) 2025 Jurnal Teknologi Informasi dan Pendidikan
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
.png)
