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All Journal ADITYA - Pendidikan Bahasa dan Sastra Jawa Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Jurnal Psikologi BELAJEA: Jurnal Pendidikan Islam JURNAL PENDIDIKAN TAMBUSAI Jurnal Review Pendidikan dan Pengajaran (JRPP) Building of Informatics, Technology and Science WACANA Instructional Development Journal Psikobuletin: Buletin Ilmiah Psikologi Jurnal Psikologi Islam HEALTHY: Jurnal Inovasi Riset Ilmu Kesehatan Innovative: Journal Of Social Science Research Indev: Literasi Media Dakwah dan Pengembangan Masyarakat Jurnal Kesehatan Cendikia Jenius Jurnal Pengabdian Kepada Masyarakat Bersinergi Inovatif
Reni Susanti
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Religion Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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Journal : Building of Informatics, Technology and Science

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Pengelompokan Tingkat Stres Akademik Pada Mahasiswa Menggunakan Algoritma K-Medoids Nurfadilah, Nova Siska; Budianita, Elvia; Nazir, Alwis; Insani, Fitri; Susanti, Reni
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7409

Abstract

Academic stress is one of the common problems issues by university students due to heavy with heavy workloads, grade pressure, and various academic This condition can have a negatively impact on mental health, productivity and overall academic performance. In the long term, unmaged stress may lead serious psychological disorders. Therefore, it is important to accurately identify and classify the levels of academic stress. This study aims to cluster students’ academic stress levels by utilizing the K-Medoids algorithm. The data analyzed in the research were collected through questionnaires that were filled out by 507 students from the 2021-2023 cohorts, based on a modified version of the Perception of Academic Stress Scale (PASS). The results show that the K-medoids algorithm successfully clustered the data in 2 groups: cluster 0, which represents a moderate stress level with 212 students, and cluster 1, which indicates a high stress level with 295 students. This high-stress cluster exhibited higher average cores on questions 12 and 13 (score 3-5), which fall under the favorable category and are suspected to be the main triggers of academic stress among students in this group. Based on two evalutation metrics-Silhouette Coeficient and Davies-Bouldin Index (DBI)-it can be concluded that the optimal number of clusters for this data set is K=2. However, the clustering separation was not optimal due to he variation in study programs and the uneven distribution of respondets across academic years. This research is expected to provide direction the development intervation policies and strategies to support student welfare.
Penerapan Algoritma K-Means Untuk Mengelompokkan Tingkat Stres Akademik Pada Mahasiswa Wiranti, Lusi Diah; Budianita, Elvia; Nazir, Alwis; Insani, Fitri; Susanti, Reni
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7410

Abstract

Academic stress is a prevalent concern among university students, often arising from various challenges within the academic environment. These challenges may include tight assignment deadlines, elevated expectations from both lecturers and parents, ineffective time management, and negative self-assessment. If left unaddressed, such stress can negatively impact students’ academic performance and mental well-being. This study focuses on categorizing student academic stress levels using the K-Means clustering algorithm. Data were collected from 507 participants through a customized version of the Perception of Academic Stress Scale (PASS) questionnaire, adapted to suit the study context. Prior to analysis, the data were preprocessed and converted into a numerical format. Clustering was performed using Python on the Google Colab platform. To assess the clustering performance, two evaluation metrics were used: the Davies-Bouldin Index (DBI) and the Silhouette Coefficient. Lower DBI values suggest that the clusters formed are more compact and distinct from each other, while higher Silhouette values indicate better clustering performance. From the evaluation, the best clustering result was found when the number of clusters was 2, with a DBI score of 1.43 and a Silhouette score of 0.27. Nonetheless, these values still fall short of the ideal range, likely due to the heterogeneous nature of the data, as participants came from five different departments within the Faculty of Science and Technology. Moreover, the number of responses varied across academic years (2021–2023). Cluster 1 comprised 229 students identified as having low levels of academic stress, as shown by their lower questionnaire scores. In contrast, Cluster 2 consisted of 278 students with higher levels of stress, as reflected in their higher scores (ranging from 3 to 5) on positively worded items.
Co-Authors Al Iman, Abi Hakim Az Zahra, Farah Darma Aprianda, Adi Desma Husni, Desma Deswita Deswita Efitra Elvia Budianita Endarini, Lully Hanni Fadriati Fadriati Fitri Insani Hadi, Cipto Ikhwanisifa Ikhwanisifa Kasmiruddin " Khaliq, Iyas Nur Komalasari, Yola Krihariyani, Dwi Maya Sari, Vera Mutyarini, Afiska Nazir, Alwis Nurfadilah, Nova Siska Prayuti, Yuyut Ramadini, Indri Raudatussalamah, . Reza Pertiwi Salmiyati Salmiyati Sari, Deswita SEPTI WULANDARI Sihombing, Imelda Siska, Feby Sri Supriyantini Suliati Suliati Vebrianto, Rian Wiranti, Lusi Diah Yusardi, Romi Zubaidah Amir MZ