HomeQSU Research Journalvol. 10 no. 1 (2021)


Melidiossa V. Pagudpud | Roselle M. Soriano | Jennifer O. Serrano

Discipline: Computer Science



People throughout the world are stressed about the COVID-19 outbreak. Everyone, including young people and university students are suffering. Proper stress management is particularly challenging for students because they confront numerous challenges while adjusting to their new academic routines. This study used the Knowledge Discovery in Databases (KDD) approach to extract knowledge from a dataset of students' stress levels gathered at Quirino State University Cabarroguis Campus in the Province of Quirino. The students must be clustered in order to conceptualize and implement stress management programs for the students on the campus. The RapidMiner tool was used to examine the DensityBased Spatial Clustering of Applications with Noise (DBSCAN), K-Means, and KMedoid algorithms. The silhouette indices of the various clustering methods were examined, and the results revealed that the K-Means algorithm with k = 3 and a silhouette index of 0.399 is the best clustering strategy for grouping the students. The tolerable group (cluster 0) had 223 students, the positive group (cluster 1) had 222 students, and the toxic group had 44 students (cluster 2). The data mining approach used in this study is critical for extracting meaningful information from the dataset in order to better understand the students' stress levels, which serves as a solid foundation for developing a campus students' stress management program.


  1. Chander, S., & Vijaya, P. (2021). Unsupervised learning methods for data clustering. In Binu, D., & Rajakumar, B. R. (Eds.) Artificial Intelligence in Data Mining (pp. 41-64). Academic Press. https://doi.org/10.1016/B978-0-12-820601-0.00002-1
  2. Ganesan, Y., Talwar, P., Fauzan, N., & Oon, Y. B. (2018). Study on Stress Level and Coping Strategies among Undergraduate Students. Journal of Cognitive Sciences and Human Development, 3(2), 37-47. https://doi.org/10.33736/jcshd.787.2018
  3. Hassani, M., & Seidl, T. (2017). Using internal evaluation measures to validate the quality of diverse stream clustering algorithms. Vietnam Journal of Computer Science, 4, 171–183. https://doi.org/10.1007/s40595-016-0086-9
  4. Johnstone, J. M., Roake, C., Sheikh, I., Mole, A., Nigg, J. T., & Oken, B. (2016). School-based mindfulness intervention for stress reduction in adolescents: Design and methodology of an open-label, parallel group, randomized controlled trial. Contemporary clinical trials communications, 4, 99–104. https://doi.org/10.1016/j.conctc.2016.07.001
  5. Mohamad, S. K., & Tasir, Z. (2013). Educational data mining: A review. Procedia - Social and Behavioral Sciences, 97, 320 – 324. doi: 10.1016/j.sbspro.2013.10.240
  6. Nivethitha, K., & Vijayalakshmi, S. (2021). Survey on Data Mining Techniques, Process and Algorithms. Journal of Physics: Conference Series, 1947, 012052. DOI 10.1088/1742-6596/1947/1/012052
  7. Rani, J. M., & Parthipan, L. (2012). Clustering analysis by improved particle swarm optimization and k-means algorithm. IET Chennai 3rd International on Sustainable Energy and Intelligent Systems. doi: 10.1049/cp.2012.2195
  8. Regehr, C., Glancy, D., & Pitts, A. (2013). Interventions to reduce stress in university students: a review and meta-analysis. Journal of affective disorders, 148(1), 1–11. https://doi.org/10.1016/j.jad.2012.11.026
  9. Ristoski, P., & Paulheim, H. (2016). Semantic Web in data mining and knowledge discovery: A comprehensive survey. Journal of Web Semantics, 36, 1-22. https://doi.org/10.1016/j.websem.2016.01.001
  10. Roseline, R., Jenitha, G., & Amirhtaraj, H. (2014). Analysis and application of clustering techniques in data mining. International Journal of Computing Algorithm, 03, 910-912.
  11. Scott, E. (2020). Top 10 Stress Management Techniques for Students. Verywell Mind. https://www.verywellmind.com/top-school-stress-relievers-for-students-3145179
  12. Sharma, N., Bajpai, A., & Lito, R. (2012). Comparison of the various clustering algorithms of Weka. International Journal of Emerging Technology and Advanced Engineering, 2(5), 73-80.
  13. Yasmin, H., Khalil, S., & Mazhar, R. (2020). COVID 19: stress management among students and its impact on their effective learning. International Technology and Education Journal, 4(2), 65-74.