University Students Stress Detection During Final Report Subject by Using NASA TLX Method and Logistic Regression

  • Alfita Khairah Universitas Syiah Kuala
  • Melinda Universitas Syiah kuala
  • Iskandar Hasanuddin Universitas Syiah Kuala
  • Didi Asmadi Universitas Syiah Kuala
  • Riski Arifin Universitas Syiah Kuala
  • Rizka Miftahujjannah Universitas Syiah Kuala
Keywords: stress, wearable system, NASA-TLX, Heart Rate, Body Temperature

Abstract

Stress is a psychological response that occurs when someone faces pressure or demands that exceed their ability to adapt. In the context of a final-year student, stress is often a significant problem due to academic pressure, such as completing final assignments, as well as demands to immediately prepare to enter the workforce and demands to immediately prepare to enter the workforce. Research shows that stress that is not managed properly can cause various negative effects, such as sleep disorders and decreased cognitive function. This study aimed to identify and analyze stress levels among final-year students who completed a final report by integrating physiological and psychological data. In this study, 30 students were assessed using a wearable system to obtain physiological data, such as heart rate and body temperature, while subjective assessments were carried out using the NASA-TLX method to measure mental workload. The results showed that 19 out of 30 respondents experienced significant levels of stress and 11 respondents were in normal conditions, with the main causal factors including high academic pressure and distance regarding the future. In addition, the logistic regression analysis applied in this study succeeded in developing a predictive model with an accuracy of 94% in identifying students' stress conditions. This shows that this method is sufficiently accurate for detecting stress symptoms in final-year students.

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References

S. de Vries et al., “Real-time stress detection based on artificial intelligence for people with an intellectual disability.,” Assist. Technol., vol. 36, no. 3, pp. 232–240, May 2024, doi: 10.1080/10400435.2023.2261045.

S. B. Dasari, C. T. Mallareddy, S. Annavarapu, and T. T. Garike, “Detection of Mental Stress Levels Using Electroencephalogram Signals(EEG),” in 2023 2nd International Conference on Futuristic Technologies (INCOFT), 2023, pp. 1–6. doi: 10.1109/INCOFT60753.2023.10425089.

B. Bervell and H. Al-Samarraie, “Wearable devices for mental health diagnosis: A systematic review and recommendations,” J. Affect. Disord., vol. 285, pp. 445–454, 2021, doi: 10.1016/j.jad.2021.03.085.

S. Gedam and S. Paul, “A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques,” IEEE Access, vol. 9, pp. 84045–84066, 2021, doi: 10.1109/ACCESS.2021.3085502.

M. M Khayyat et al., “An improved biometric stress monitoring solution for working employees using heart rate variability data and Capsule Network model.,” PLoS One, vol. 19, no. 12, p. e0310776, 2024, doi: 10.1371/journal.pone.0310776.

S. Slimmen, O. Timmermans, K. Mikolajczak-Degrauwe, and A. Oenema, “How stress-related factors affect mental wellbeing of university students A cross-sectional study to explore the associations between stressors, perceived stress, and mental wellbeing.,” PLoS One, vol. 17, no. 11, p. e0275925, 2022, doi: 10.1371/journal.pone.0275925.

S. Immanuel, M. N. Teferra, M. Baumert, and N. Bidargaddi, “Heart Rate Variability for Evaluating Psychological Stress Changes in Healthy Adults: A Scoping Review.,” Neuropsychobiology, vol. 82, no. 4, pp. 187–202, 2023, doi: 10.1159/000530376.

D. Ichwana, R. Z. Ikhlas, and S. Ekariani, “Heart Rate Monitoring System During Physical Exercise for Fatigue Warning Using Non-invasive Wearable Sensor,” in 2018 International Conference on Information Technology Systems and Innovation (ICITSI), 2018, pp. 497–502. doi: 10.1109/ICITSI.2018.8696039.

T. Sollu, A. Alamsyah, and E. Setijadi, “Heartbeat and Body Temperature Monitoring System Based on Artificial Neural Networks,” J. Ecotipe (Electronic, Control. Telecommun. Information, Power Eng., vol. 9, no. 2 SE-Articles, Oct. 2022, doi: 10.33019/jurnalecotipe.v9i2.2896.

C. Panari, D. Guglielmi, A. Ricci, M. C. Tabanelli, and F. S. Violante, “Assessing and improving health in the workplace: an integration of subjective and objective measures with the STress Assessment and Research Toolkit (St.A.R.T.) method.,” J. Occup. Med. Toxicol., vol. 7, no. 1, p. 18, Sep. 2012, doi: 10.1186/1745-6673-7-18.

R. Miftahujjannah, M. Melinda, S. Syahrial, Y. Yunidar, V. P. Hasan, and Z. Taqiuddin, “Wearable Detection System for Early Symptoms of Tantrums in Children Based on IoT,” in 2024 IEEE 14th Symposium on Computer Applications & Industrial Electronics (ISCAIE), 2024, pp. 137–142. doi: 10.1109/ISCAIE61308.2024.10576296.

A. Bozorgmehr, A. Thielmann, and B. Weltermann, “Chronic stress in practice assistants: An analytic approach comparing four machine learning classifiers with a standard logistic regression model.,” PLoS One, vol. 16, no. 5, p. e0250842, 2021, doi: 10.1371/journal.pone.0250842.

M. R. Misna Rais; Utami, Iut Tri, “Analisis Regresi Logistik Biner Untuk Mengklasifikasi Penderita Hipertensi Berdasarkan Kebiasaan Merokok Di RSU Mokopido Toli-Toli,” Nat. Sci. J. Sci. Technol., vol. 7, no. 3, pp. 341 – 348, 2018, [Online]. Available: http://jurnal.untad.ac.id/jurnal/index.php/ejurnalfmipa/article/view/11464

M. Moghimbeygi and A. Nodehi, “Multinomial Principal Component Logistic Regression on Shape Data,” J. Classif., vol. 39, no. 3, pp. 578–599, 2022, doi: 10.1007/s00357-022-09423-x.

S. Aristizabal et al., “The Feasibility of Wearable and Self-Report Stress Detection Measures in a Semi-Controlled Lab Environment,” IEEE Access, vol. 9, pp. 102053–102068, 2021, doi: 10.1109/ACCESS.2021.3097038.

R. Navea, P. Buenvenida, and C. Cruz, “Stress Detection using Galvanic Skin Response: An Android Application,” J. Phys. Conf. Ser., vol. 1372, p. 12001, Nov. 2019, doi: 10.1088/1742-6596/1372/1/012001.

A. Putra, R. Pratama, and A. Farahdiansari, “Analysis Of Mental Workload With NASA- TLX Method On Employees Of Kareb Bojonegoro Cooperative,” J. Inf. Syst. Technol. Eng., vol. 1, no. 3, pp. 95–103, Sep. 2023, doi: 10.61487/jiste.v1i3.28.

M. A. Adrian, M. R. Widiarto, and R. S. Kusumadiarti, “Health Monitoring System Dengan Indikator Suhu Tubuh, Detak Jantung Dan Saturasi Oksigen Berbasis Internet of Things (IoT),” Petik J. Pendidik. Teknol. Inf. Dan Komun., vol. 7, no. 2, pp. 108–118, Sep. 2021, doi: 10.31980/petik.v7i2.1235.

R. Hernandez, S. C. Roll, H. Jin, S. Schneider, and E. A. Pyatak, “Validation of the National Aeronautics and Space Administration Task Load Index (NASA-TLX) adapted for the whole day repeated measures context.,” Ergonomics, vol. 65, no. 7, pp. 960–975, Jul. 2022, doi: 10.1080/00140139.2021.2006317.

L. A. S. Silalong, S. Sari, D. Arfianto, I. Pujiyanto, and D. H. Hanifah, “Analysis of mental workload final year students due to online learning with NASA-TLX method,” HEARTY, vol. 12, no. 2, pp. 301–314, Mar. 2024, doi: 10.32832/hearty.v12i2.5458.

R. Taslim and A. U. Afifah, “Beban Kerja Fisik Dan Mental Welder Menggunakan Metode Nordic Body Map Dan Metode NASA TLX,” in Seminar Nasional Teknologi Informasi, Komunikasi dan Industri (SNTIKI-13), Pekanbaru: UIN Sultan Syarif Kasim Riau, 2021. [Online]. Available: https://ejournal.uin-suska.ac.id/index.php/SNTIKI/issue/viewIssue/956/64

E. Aktas Potur, Ş. Toptancı, and M. Kabak, “Mental Workload Assessment in Construction Industry with Fuzzy NASA-TLX Method BT,” in The Sixteenth International Conference on Management Science and Engineering Management, J. Xu, F. Altiparmak, M. H. A. Hassan, F. P. García Márquez, and A. Hajiyev, Eds., Cham: Springer International Publishing, 2022, pp. 729–742.

S. Fauzi, “Analisis Beban Kerja Mental Menggunakan Metode Nasa-TLX untuk Mengevaluasi Beban Kerja Operator pada Lantai Produksi PT PP Londsumatra Indonesia Tbk, Turangie Palm Oil Mili, Kabupaten Langkat,” Universitas Medan Area, 2017. [Online]. Available: https://repositori.uma.ac.id/handle/123456789/7972

R. Iliyasu and I. Etikan, “Comparison of quota sampling and stratified random sampling,” Int. J. Biom., vol. 10, no. 1, pp. 24–27, Aug. 2021, doi: 10.15406/bbij.2021.10.00326.

S. Hofstätter, S.-C. Lin, J.-H. Yang, J. Lin, and A. Hanbury, “Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling,” in Proceedings of the 44 th International ACM SIGIR Conferenceon Research and Development in Information Retrieval (SIGIR’21), New York: ACM, Jul. 2021, pp. 113–122. doi: 10.1145/3404835.3462891.

S. Mukodimah and C. Fauzi, “Comparison Of Tree Implementation, Regression Logistics, and Random Forest To Detect Iris Types,” J. TAM (Technology Accept. Model., vol. 12, no. 2, p. 149, Nov. 2021, doi: 10.56327/jurnaltam.v12i2.1074.

B. R. A. Febrilia, S. Rahayu, and B. D. Korida, “Ordinal Logistic Regression Analysis of Factors Affecting the Length of Student Study,” J. Mat. MANTIK, vol. 5, no. 1, pp. 28–34, May 2019, doi: 10.15642/mantik.2019.5.1.28-34.

T. Iqbal, A. Elahi, W. Wijns, and A. Shahzad, “Exploring Unsupervised Machine Learning Classification Methods for Physiological Stress Detection.,” Front. Med. Technol., vol. 4, p. 782756, 2022, doi: 10.3389/fmedt.2022.782756.

K. Takahashi, K. Yamamoto, A. Kuchiba, and T. Koyama, “Confidence interval for micro-averaged F1 and macro-averaged F1 scores,” Appl. Intell., vol. 52, no. 5, pp. 4961–4972, 2022, doi: 10.1007/s10489-021-02635-5.

D. M. W. Powers, “Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation,” J. Mach. Learn. Technol., vol. 2, pp. 37–63, Jan. 2020, doi: https://doi.org/10.48550/arXiv.2010.16061.

P. Siirtola and J. Röning, “Comparison of Regression and Classification Models for User-Independent and Personal Stress Detection.,” Sensors (Basel, Switzerland), vol. 20, no. 16. Switzerland, p. 4402, Aug. 2020. doi: 10.3390/s20164402.

K. S. Erlis Manita, Marty Mawarpury, Maya Khairani, “Hubungan Stres dan Kesejahteraan (Well-being) dengan Moderasi Kebersyukuran,” Gadjah Mada J. Psychol., vol. 5, no. 2, pp. 178–186, 2019, doi: 10.22146/gamajop.50121.

Published
2025-05-13
How to Cite
Khairah, A., Melinda, Hasanuddin, I., Asmadi, D., Arifin, R., & Miftahujjannah, R. (2025). University Students Stress Detection During Final Report Subject by Using NASA TLX Method and Logistic Regression. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 9(3), 215 - 226. https://doi.org/10.29207/resti.v9i3.6401
Section
Information Systems Engineering Articles