Klasifikasi Tingkat Stres Mahasiswa Dalam Penyelesaian Tugas Akhir Menggunakan Naïve Bayes Dan K-Nearest Neighbor

Lenni Pefrianti, Ibnu Rasyid Munthe, Irmayanti Irmayanti, Masrizal Masrizal

Abstract


This study aims to analyze the stress levels of final-year students and compare the performance of Naïve Bayes and K-Nearest Neighbor (KNN) algorithms in stress classification. Data were collected from 82 respondents through a questionnaire consisting of seven variables (S1–S7) measuring factors contributing to stress, which were classified into low, moderate, and high stress levels. The results show that both algorithms can classify student stress effectively, with Naïve Bayes achieving the highest accuracy (90.15%) compared to KNN (87.72%). Distribution analysis by study program indicates that Agrotechnology has the highest proportion of students with high stress (42.86%), followed by Information Systems (40.63%) and Information Technology (13.64%). This study provides insights for the university to offer targeted support through counseling or stress management workshops.


Full Text:

PDF

References


Alamsyah, M. F., & Wijaya, A. (2025). Perbandingan Metode KNN dan Naïve Bayes dalam Deteksi Tingkat Stres Berdasarkan Ekspresi Wajah. Jurnal Informatika: Jurnal Pengembangan IT, 10(2), 359–369. https://doi.org/10.30591/jpit.v10i2.8513

Chai, S. (2025). The Impact of Adolescent Academic Stress on Academic Achievement. Lecture Notes in Education Psychology and Public Media, 109(1), 202–208. https://doi.org/10.54254/2753-7048/2025.ND26080

Dafitrii, H., & Sundari stth, S. (2022). SISTEM PAKAR MENDETEKSI TINGKAT STRES MAHASISWA HARAPAN DALAM PENYUSUNAN SKRIPSI TEKNIK INFORMATIKA DENGAN MENGGUNAKAN METODE TEOREMA BAYES. Syntax : Journal of Software Engineering, Computer Science and Information Technology, 2(2), 165–171. https://doi.org/10.46576/syntax.v2i2.1678

Dewi Nasien, Darwin, R., Cia, A., Leo Winata, A., Go, J., M.C, R., Charles Wijaya, R., & Charles Lo, K. (2024). Perbandingan Implementasi Machine Learning Menggunakan Metode KNN, Naive Bayes, dan Logistik Regression Untuk Mengklasifikasi Penyakit Diabetes. JEKIN - Jurnal Teknik Informatika, 4(1), 10–17. https://doi.org/10.58794/jekin.v4i1.640

Divisi, D., Leonardo, G. Di, Zaccagna, G., & Crisci, R. (2017). Basic statistics with Microsoft Excel : a review. 9(6), 1734–1740. https://doi.org/10.21037/jtd.2017.05.81

Djoar, R. K., Putu, A., Anggarani, M., Airlangga, U., & Fisioterapi, P. S. (2024). FAKTOR - FAKTOR YANG MEMPENGARUHI STRESS AKADEMIK. 6(1), 52–59.

Najjar, E., & Majeed Breesam, A. (2023). Supervised Machine Learning a Brief Survey of Approaches. Al-Iraqia Journal for Scientific Engineering Research, 2(4), 71–82. https://doi.org/10.58564/IJSER.2.4.2023.121

Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x

Singh, A., Singh, K., Kumar, A., Shrivastava, A., & Kumar, S. (2024). Machine Learning Algorithms for Detecting Mental Stress in College Students. 2024 IEEE 9th International Conference for Convergence in Technology (I2CT), 1–5. https://doi.org/10.1109/I2CT61223.2024.10544243

Susanti, L. (2024). Klasifikasi Tingkat Stress pada Mahasiswa Teknik Informatika dalam Melakukan Perkuliahan Metode Hybrid Menggunakan Algoritma Naive Bayes. STRING (Satuan Tulisan Riset Dan Inovasi Teknologi), 8(3), 243. https://doi.org/10.30998/string.v8i3.17096

Tapidingan, Y. C., & Paseru, D. (2020). Comparative Analysis of Classification Methods of KNN and Naïve Bayes to Determine Stress Level of Junior High School Students. Indonesian Journal of Information Systems, 2(2), 80–89. https://doi.org/10.24002/ijis.v2i2.3035

Thiyagarajan, A., James, T. G., & Marzo, R. R. (2022). Psychometric properties of the 21-item Depression, Anxiety, and Stress Scale (DASS-21) among Malaysians during COVID-19: a methodological study. Humanities and Social Sciences Communications, 9(1), 220. https://doi.org/10.1057/s41599-022-01229-x

Wibisono, B., Machmud, A., Suryani, N., & Yunita, Y. (2025). Analisis Sentimen Cyberbullying Pada Komentar X Menggunakan Metode Naïve Bayes. Computer Science (CO-SCIENCE), 5(1), 8–16. https://doi.org/10.31294/coscience.v5i1.5152

Yuda, A. K., Resita, C., Nurwansyah, R., Gani, R. A., Németh, Z., & Setiawan, E. (2022). Confidence, Academic Stress, Coping Strategies as Predictors of Student Academic Achievement in Physical Education Classes During Covid-19. Physical Education Theory and Methodology, 22(2), 180–187. https://doi.org/10.17309/tmfv.2022.2.05




DOI: https://doi.org/10.36987/jcoins.v7i1.9060

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Hasil gambar untuk committee on publication ethics logo

Jurnal ini mengikuti pedoman dari Committee on Publication Ethics (COPE) dalam menghadapi semua aspek etika publikasi dan, khususnya, bagaimana menangani kasus penelitian dan kesalahan publikasi. Pernyataan ini menjelaskan etika perilaku semua pihak yang terlibat dalam proses penerbitan artikel di jurnal ini, termasuk Penulis, Pemimpin Redaksi, Dewan Redaksi, Mitra Bebestari, dan Penerbit (Akademi Kepolisian Republik Indonesia). Journal of Computer Science and Information System(JCoInS) berkomitmen untuk mengikuti praktik terbaik tentang masalah etika, kesalahan, dan pencabutan. Pencegahan malpraktek publikasi merupakan salah satu tanggung jawab penting dewan redaksi. Segala jenis perilaku tidak etis tidak dapat diterima, dan jurnal tidak mentolerir plagiarisme dalam bentuk apa pun.


Journal of Computer Science and Information System(JCoInS)

Journal URL: https://jurnal.ulb.ac.id/index.php/JCoInS/index
Journal DOI: 10.36987/jcoins
E-ISSN: 2747-2221

Alamat Redaksi :
Fakultas Sains dan Teknologi, Universitas Labuhanbatu
Gedung Fakultas Sains dan Teknologi,
Jalan Sisingamangaraja No.126 A KM 3.5 Aek Tapa, Bakaran Batu, Rantau Sel., Kabupaten Labuhan Batu, Sumatera Utara 21418