Comparison of K-Nearest Neighbor, Naïve Bayes, and C4.5 Algorithms for Predicting Academic Stress Risk in Students Based on Psychological Survey Data
DOI:
https://doi.org/10.30871/jaic.v10i1.11932Keywords:
Academic Stress, K-Nearest Neighbor, Naïve Bayes, C4.5Abstract
Academic stress is a psychological problem experienced by many students and can have an impact on learning achievement, mental health, and quality of life. This study aims to compare the performance of the K-Nearest Neighbor (KNN), Naïve Bayes, and C4.5 (Decision Tree) algorithms in predicting the level of academic stress risk in students based on psychological survey data. Data were obtained from 700 active students at Ngudi Waluyo University through a questionnaire covering physiological, psychological, and behavioral aspects, with a total of 15 indicators using a Likert scale. The data then underwent pre-processing, labeling, standardization, and division into training and test data with a ratio of 80:20. The evaluation was conducted using the Accuracy, Precision, Recall, F1-Score, Confusion Matrix, and AUC-ROC metrics. The results showed that the Naïve Bayes algorithm performed best with an accuracy of 93.26%, precision of 93.35%, recall of 92.26%, and F1-score of 92.80%. The KNN algorithm was in second place with an accuracy of 91.43%, while the C4.5 algorithm had the lowest performance with an accuracy of 80.60%. Based on these results, Naïve Bayes is recommended as the most optimal algorithm for predicting academic stress risk in students using psychological survey data. This study is expected to assist educational institutions in identifying students at risk of stress early on and supporting the development of more effective prevention strategies.
Downloads
References
[1] D. S. Anastasya Carity, E. Simamora, Z. Indra, and S. Manullang, “Implementasi Algoritma Naïve Bayes Dalam Melakukan Klasifikasi Tingkat Stres Siswa SMA,” 2025.
[2] S. Arya, Anju, and N. A. Ramli, “Predicting the stress level of students using Supervised Machine Learning and Artificial Neural Network (ANN),” Indian J. Eng., vol. 21, no. 56, 2024, doi: 10.54905/disssi.v21i55.e9ije1684.
[3] D. Toresa et al., “Perbandingan Algoritma C4.5 Dan Naïve Bayes Untuk Mengukur Tingkat Kepuasan Mahasiswa Dalam Penggunaan Edlink,” J. Teknol. Dan Sist. Inf. Bisnis, vol. 5, no. 3, pp. 250–256, Jul. 2023, doi: 10.47233/jteksis.v5i3.855.
[4] A. R. Sinadia, “Sumikolah: Jurnal Ilmu Pendidikan Aplikasi dan Praktik Konstruksi Skala Psikologi sebagai Instrumen Pengukuran dalam Penelitian Pendidikan,” vol. 2, no. 1, 2024.
[5] I. Novita Sari, L. Fakih Lidimilah, and A. Lutfi, “Proceeding National Conference Of Research And Community Service Sisi Indonesia 2025 Analisis Perbandingan Algoritma Decision Tree, Random Forest, Dan Naïve Bayes Dalam Klasifikasi Gangguan Tidur.” [Online]. Available: https://conference.sinesia.id/ncrcs-sinesia
[6] S. S. Berutu, H. Budiati, J. Jatmika, and F. Gulo, “Data preprocessing approach for machine learning-based sentiment classification,” J. INFOTEL, vol. 15, no. 4, pp. 317–325, Nov. 2023, doi: 10.20895/infotel.v15i4.1030.
[7] A. P. Permana, K. Ainiyah, and K. F. H. Holle, “Analisis Perbandingan Algoritma Decision Tree, kNN, dan Naive Bayes untuk Prediksi Kesuksesan Start-up,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 6, no. 3, 2021, doi: 10.14421/jiska.2021.6.3.178-188.
[8] R. P. Lestari, N. Surojudin, and E. Budiarto, “Klasifikasi Tingkat Stres pada Mahasiswa Tingkat Akhir menggunakan Metode Naïve Bayes di Universitas Pelita Bangsa Stress Level Classification of Final-Year Students Using Naïve Bayes at Universitas Pelita Bangsa,” 2025. [Online]. Available: https://jurnal.unimed.ac.id/2012/index.php/cess
[9] A. Al Arif, M. Firdaus, Y. Maruhawa, S. AMIK Riau, and J. Purwodadi Panam, “SENTIMAS: Seminar Nasional Penelitian dan Pengabdian Masyarakat Comparison of Data Mining Methods for Predition of Rainfall with C4.5, Naïve Bayes, and KNN Algorithm Perbandingan Metode Data Mining untuk Prediksi Curah Hujan dengan Algoritma C4.5, Naïve B.” [Online]. Available: https://journal.irpi.or.id/index.php/sentimas
[10] M. F. Indriawan, J. P. P. Naibaho, and M. Sanglise, “Analisis Komparasi Algoritma Naïve Bayes Dan C4.5 Dalam Memprediksi Kelulusan Mahasiswa (Studi Kasus: Jurusan S1 Teknik Informatika Universitas Papua),” JISTECH J. Inf. Sci. Technol., vol. 11, no. 2, 2023, doi: 10.30862/jistech.v11i2.107.
[11] N. Renaningtias, T. Eka Putri, E. Putri Purwandari, Y. Stainly Ritonga, and P. Sistem, “Studi Komparasi Algoritma Decision Tree C4.5 dan K-Nearest Neighbor pada Klasifikasi Masa Studi dan Tingkat Stres Mahasiswa”
[12] P. Prasetyawan, I. Ahmad, R. I. Borman, Ardiansyah, Y. A. Pahlevi and D. E. Kurniawan, "Classification of the Period Undergraduate Study Using Back-propagation Neural Network," 2018 International Conference on Applied Engineering (ICAE), Batam, Indonesia, 2018, pp. 1-5, doi: 10.1109/INCAE.2018.8579389.
[13] Y. C. Tapidingan and D. Paseru, “Comparative Analysis of Classification Methods of KNN and Naïve Bayes to Determine Stress Level of Junior High School Students”, Indonesian J. of Inf. Syst., vol. 2, no. 2, pp. 80–89, Feb. 2020.
[14] Herman, H. Darwis, Nurfauziyah, R. Puspitasari, D. Widyawati and A. Faradibah, "Comparative Analysis of Anxiety Disorder Classification Using Algorithm Naïve Bayes, Decision Tree and K-NN," 2025 19th International Conference on Ubiquitous Information Management and Communication (IMCOM), Bangkok, Thailand, 2025, pp. 1-6, doi: 10.1109/IMCOM64595.2025.10857485.
[15] I. Gunawan and A. P. Widyassari, “Integrating Psychological Stress Indicators with Academic Data for Student Dropout Prediction: A Decision Tree and Expert System Approach ”, ELINVO, vol. 10, no. 2, pp. 131–146, Dec. 2025.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Nur Annisa Widya Pertiwi, Iwan Setiawan Wibisono
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
