Machine Learning untuk Deteksi Stres Pelajar: Perceptron sebagai Model Klasifikasi Efektif untuk Intervensi Dini

Febrina Nabila Zahrah; Muljono Muljono

doi:10.29408/edumatic.v8i2.28011

Authors

Febrina Nabila Zahrah Program Studi Teknik Informatika, Universitas Dian Nuswantoro https://orcid.org/0009-0008-4787-6604
Muljono Muljono Program Studi Teknik Informatika, Universitas Dian Nuswantoro https://orcid.org/0000-0001-6059-3571

DOI:

https://doi.org/10.29408/edumatic.v8i2.28011

Keywords:

machine learning, perceptron, stress detection, student stress

Abstract

Stress is a serious challenge for students that can negatively impact physical health, mental well-being, and academic performance. However, accurate and effective stress detection approaches to support early intervention are still limited. This study aims to evaluate machine learning models for detecting student stress levels with optimal accuracy to facilitate early intervention. The research employs a quantitative approach using a dataset containing 1,100 student samples from Nepal, encompassing 20 stress-related features from psychological, social, academic, environmental, and physiological aspects. Data were collected via a self-report questionnaire, processed with StandardScaler scaling, and analyzed using 10-fold cross-validation. The models tested include Perceptron, Gradient Boosting Trees Classifier (GBTC), Naive Bayes (NB), Logistic Regression (LR), and AdaBoost. The results show that Perceptron performed the best with an accuracy of 97.27%, followed by NB (95.45%), GBTC (94.54%), LR (94.54%), and AdaBoost (93.63%). Perceptron, with its advantage in linearity and evaluation through 10-fold cross-validation, shows great potential as an effective classification model for student stress detection, which can accelerate early intervention and enhance student well-being and learning environments.

References

Alkhamees, B. F. (2022). An optimized single-layer perceptron-based approach for cardiotocography data classification. International Journal of Advanced Computer Science and Applications, 13(10), 239-245. https://doi.org/10.14569/IJACSA.2022.0131030

Armansyah, A., & Ramli, R. K. (2022). Model prediksi kelulusan mahasiswa tepat waktu dengan metode Naïve Bayes. Edumatic: Jurnal Pendidikan Informatika, 6(1), 1–10. https://doi.org/10.29408/edumatic.v6i1.4789

Arya, S., Anju, A., & Azuana Ramli, N. (2024). Predicting the stress level of students using supervised machine learning and artificial neural networks (ANN). Indian Journal of Engineering, 21(56), 1–24. https://doi.org/10.54905/disssi.v21i55.e9ije1684

Asif, S., Muddassar, A., Shahzad, T. Z., Raouf, M., & Pervaiz, T. (2020). Frequency of depression, anxiety, and stress among university students. Pakistan Journal of Medical Sciences, 36(5), 971–976. https://doi.org/10.12669/pjms.36.5.1873

Emmer, C., Bosnjak, M., & Mata, J. (2020). The association between weight stigma and mental health: A meta-analysis. Obesity Reviews, 21(1), e12935. https://doi.org/10.1111/obr.12935

Góngora Alonso, S., Marques, G., Agarwal, D., De La Torre Díez, I., & Franco-Martín, M. (2022). Comparison of machine learning algorithms in the prediction of hospitalized patients with schizophrenia. Sensors, 22(7), 2517. https://doi.org/10.3390/s22072517

Iqbal, T., Elahi, A., Wijns, W., & Shahzad, A. (2023). Cortisol detection methods for stress monitoring in connected health. Health Sciences Review, 6, 100079. https://doi.org/10.1016/j.hsr.2023.100079

Isabona, J., Imoize, A. L., Ojo, S., Karunwi, O., Kim, Y., Lee, C.-C., & Li, C.-T. (2022). Development of a multilayer perceptron neural network for optimal predictive modeling in urban microcellular radio environments. Applied Sciences, 12(11), 5713. https://doi.org/10.3390/app12115713

Jiménez-Mijangos, L. P., Rodríguez-Arce, J., Martínez-Méndez, R., & Reyes-Lagos, J. J. (2023). Advances and challenges in the detection of academic stress and anxiety in the classroom: A literature review and recommendations. Education and Information Technologies, 28(4), 3637–3666. https://doi.org/10.1007/s10639-022-11324-w

Lai, S. B. S., Binti Md Shahri, N. H. N., Mohamad, M. B., Rahman, H. A. B. A., & Rambli, A. B. (2021). Comparing the performance of AdaBoost, XGBoost, and logistic regression for imbalanced data. Mathematics and Statistics, 9(3), 379–385. https://doi.org/10.13189/ms.2021.090320

Lyu, Z., Yu, Y., Samali, B., Rashidi, M., Mohammadi, M., Nguyen, T. N., & Nguyen, A. (2022). Back-propagation neural network optimized by K-fold cross-validation for prediction of torsional strength of reinforced concrete beam. Materials, 15(4), 1477. https://doi.org/10.3390/ma15041477

Malakouti, S. M., Menhaj, M. B., & Suratgar, A. A. (2023). The usage of 10-fold cross-validation and grid search to enhance ML methods performance in solar farm power generation prediction. Cleaner Engineering and Technology, 15, 100664. https://doi.org/10.1016/j.clet.2023.100664

Mentis, A.-F. A., Lee, D., & Roussos, P. (2024). Applications of artificial intelligence–machine learning for detection of stress: A critical overview. Molecular Psychiatry, 29(6), 1882–1894. https://doi.org/10.1038/s41380-023-02047-6

Montgomery, R. M., Antonia, M., & Vidigal Monteiro De Gouvea. (2024). Impact of chronic stress on physical and mental health: A detailed analysis. https://doi.org/10.13140/RG.2.2.35357.73445

Moreno-Ibarra, M.-A., Villuendas-Rey, Y., Lytras, M. D., Yáñez-Márquez, C., & Salgado-Ramírez, J.-C. (2021). Classification of diseases using machine learning algorithms: A comparative study. Mathematics, 9(15), 1817. https://doi.org/10.3390/math9151817

Nakie, G., Segon, T., Melkam, M., Desalegn, G. T., & Zeleke, T. A. (2022). Prevalence and associated factors of depression, anxiety, and stress among high school students in, Northwest Ethiopia, 2021. BMC psychiatry, 22(1), 739. https://doi.org/10.1186/s12888-022-04393-1

Rajeswari, S., Gomathi, R., & Sujitha, I. (2024). Tree-based multiclass learning model for physiological stress detection from students' community. 2024 International Conference on Intelligent Systems for Cybersecurity (ISCS), 1–6. https://doi.org/10.1109/ISCS61804.2024.10581339

Sari, V. R., Firdausi, F., & Azhar, Y. (2020). Perbandingan Prediksi Kualitas Kopi Arabika dengan Menggunakan Algoritma SGD, Naive Bayes, dan Random Forest. Edumatic: Jurnal Pendidikan Informatika, 4(2), 1-9.

Sekar, M., Sriramprabha, R., Sekhar, P. K., Bhansali, S., Ponpandian, N., Pandiaraj, M., & Viswanathan, C. (2020). Review—Towards wearable sensor platforms for the electrochemical detection of cortisol. Journal of The Electrochemical Society, 167(6), 067508. https://doi.org/10.1149/1945-7111/ab7e24

Shahapur, S. S., Chitti, P., Patil, S., Nerurkar, C. A., Shivannagol, V. S., Rayanaikar, V. C., Sawant, V., & Betageri, V. (2024). Decoding minds: Estimation of stress level in students using machine learning. Indian Journal of Science and Technology, 17(19), 2002–2012. https://doi.org/10.17485/IJST/v17i19.2951

Sharma, S., Singh, G., & Sharma, M. (2021). A comprehensive review and analysis of supervised learning and soft computing techniques for stress diagnosis in humans. Computers in Biology and Medicine, 134, 104450. https://doi.org/10.1016/j.compbiomed.2021.104450

Susanti, L. (2024). Klasifikasi tingkat stres pada mahasiswa Teknik Informatika dalam melakukan perkuliahan metode hybrid menggunakan algoritma Naïve Bayes. STRING (Satuan Tulisan Riset dan Inovasi Teknologi, 8(3), 243-248. https://doi.org/10.30998/string.v8i3.17096

World Health Organization. (2023). Suicide. WHO. https://www.who.int/news-room/fact-sheets/detail/suicide