Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal MES Management Journal Engineering: Journal of Mechatronics and Education
Sinta Hardianti
Unknown Affiliation
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Engineering: Journal of Mechatronics and Education

 1 
Comparison of K-Means and K-Medoids Methods in Clustering Stress Levels of 6th Semester Students Nuraini, Roswanda; Sinta Hardianti; Elkin Rivalni
Engineering: Journal of Mechatronics and Education Vol. 2 No. 1 (2025): Engineering: Journal of Mechatronics and Education
Publisher : Yayasan Insan Mulia Bima

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59923/mechatronics.v2i1.517

Abstract

Final-year students often experience high psychological pressure due to academic demands such as thesis completion, final exams, and career uncertainty. This stress can negatively affect their academic performance and overall mental health. This study aims to compare the performance of two clustering methods, K-Means and K-Medoids, in grouping stress levels among sixth-semester students. The comparison is based on three key parameters: accuracy, consistency, and computational speed. Data were collected using psychological questionnaires reflecting students’ stress symptoms and analyzed using both clustering techniques. Preliminary results indicate that K-Medoids outperforms K-Means in terms of accuracy and result stability, particularly when dealing with datasets containing outliers, while K-Means is more efficient in processing large-scale data. These findings are expected to serve as a reference for educational institutions in developing early stress detection systems based on data mining to enhance more targeted psychological support services.
Co-Authors Elkin Rivalni Kholid Murtadlo Nuraini, Roswanda