Deteksi Dini Depresi Mahasiswa Tingkat Akhir Menggunakan Algoritma Naïve Bayes dan Instrumen PHQ-9
-
Muhammad Sadikin
*
Universitas Potensi Utama, Medan,
Indonesia
- Dini Ridha Dwiki Putri Universitas Potensi Utama, Medan, Indonesia
- Azrifirizky Amanda Universitas Potensi Utama, Medan, Indonesia (*) Corresponding Author
Abstract
This study aims to analyze the performance of the Naïve Bayes algorithm in an early detection system for depression levels among final-year university students using the PHQ-9 instrument. The classification is conducted in a multi-category manner, grouping respondents into normal, mild depression, moderate depression, and severe depression categories based on the results of the PHQ-9 questionnaire. The main issues addressed in this research are the low awareness of depression symptoms and the need for a screening tool that is fast, lightweight, and accessible. Involving 188 respondents, data were collected through the PHQ-9 questionnaire and subsequently processed using the Naïve Bayes method. Model evaluation was performed using a confusion matrix and 10-fold cross-validation, resulting in an accuracy of 87.23%, a weighted average precision of 87.54%, and a weighted average recall of 87.25%. These findings demonstrate that Naïve Bayes can classify depression levels with high and stable accuracy, particularly in the moderate and severe depression categories. This study recommends the use of the Naïve Bayes algorithm as the basis for developing a web-based screening system that can be utilized by higher education institutions as a self-assessment and systematic early detection tool for depression disorders.
Downloads
References
N. Berutu, “Deteksi Tingkat Depresi Kerja Pada Guru Sekolah Dasar Menggunakan Metode Fuzzy Mamdani,” FASILKOM, vol. 14, no. 2, pp. 541–548, Aug. 2024, doi: https://doi.org/10.37859/jf.v14i2.7739.
H. Nathasya, P. Nuraini, S. Zahara, A. Thohiroh, T. Salma, and F. Rozzaqyah, “Analsis Tingkat dan Faktor Penyebab Depresi Se Asia Tenggara,” Edu Research, vol. 5, no. 1, pp. 70–80, Mar. 2024, doi: https://doi.org/10.47827/jer.v5i1.157.
D. Ridha Dwiki Putri, M. Reza Fahlevi, M. Sadikin, R. Utami, and M. Rizki Fajar Utomo, “Prediksi Tingkat Depresi Remaja Menggunakan Metode Naïve Bayes Classifier: Analisis Faktor Psikologis Dan Lingkungan,” KESATRIA: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen), vol. 5, no. 4, pp. 2034–2043, Oct. 2025, doi: https://doi.org/10.30645/kesatria.v5i4.545.g540.
R. M. Montgomery and M. A. V. M. Gouvea, “Impact of Chronic Stress on Physical and Mental Health: A Detailed Analysis,” Journal of Genetic Engineering and Biotechnology Research, vol. 6, no. 2, pp. 1–7, Aug. 2024, doi: 10.20944/preprints202408.1287.v1.
X. Yang et al., “A Bidirectional Association Between Internet Addiction And Depression: A Large-Sample Longitudinal Study Among Chinese University Students,” J Affect Disord, vol. 299, no. 10, pp. 416–424, Feb. 2022, doi: 10.1016/j.jad.2021.12.013.
D. Septiani, U. Enri, N. Sulistiyowati, T. Informatika, and U. S. Karawang, “Diagnosa Tingkat Depresi Mahasiswa Selama Masa Pandemi Covid-19 Menggunakan Algoritma Random Forest,” STRING (Satuan Tulisan Riset dan Inovasi Teknologi), vol. 6, no. 2, pp. 149–57, Dec. 2021, doi: 10.30998/string.v6i2.10361.
M. Nayan et al., “Comparison Of The Performance Of Machine Learning-Based Algorithms For Predicting Depression And Anxiety Among University Students In Bangladesh: A Result Of The First Wave Of The COVID-19 Pandemic,” Asian Journal of Social Health and Behavior, vol. 5, no. 2, pp. 75–84, Apr. 2022, doi: 10.4103/shb.shb_38_22.
N. Gede Widyawati, M. Khasanah, and E. Rasywir, “Prediksi Tingkat Stress Pada Mahasiswa Universitas Dinamika Bangsa Jambi Dalam Melakukan Perkuliahan Metode Hybrid Menggunakan Algoritma Naive Bayes,” Jurnal Manajemen Teknologi dan Sistem Informasi (JMS), vol. 1, no. 2, pp. 99–100, Apr. 2022, doi: https://doi.org/10.33998/jms.2022.2.1.44.
R. P. Sonjaya, A. R. Gintara, L. S. Riza, M. Nursalman, E. Nugraha, and D. Wahyudin, “Predicting Student Depression Using the Naive Bayes Model on the Student Depression Dataset from Kaggle,” JENTIK : Jurnal Pendidikan Teknologi Informasi dan Komunikasi, vol. 4, no. 1, pp. 9–20, Jun. 2025, doi: 10.58723/jentik.v4i1.448.
M. Rijal et al., “Prediksi Depresi: Inovasi Terkini Dalam Kesehatan Mental Melalui Metode Machine Learning,” Journal Pharmacy And Application Of Computer Sciences, vol. 2, no. 1, pp. 9–14, Feb. 2024, doi: https://doi.org/10.59823/jopacs.v2i1.47.
I. Setiawan, I. Fatah Yasin, and Y. Tri Desianti, “Komparasi Kinerja Algoritma Random Forest, Decision Tree, Naïve Bayes, dan KNN dalam Prediksi Tingkat Depresi Mahasiswa Menggunakan Student Depression Dataset,” Jurnal Ilmu Komputer Dan Teknologi (IKOMTI), vol. 6, no. 1, pp. 47–58, Feb. 2025, doi: https://doi.org/10.35960/ikomti.v6i1.1756.
A. A. Jamali, C. Berger, and R. J. Spiteri, “Identification of depression predictors from standard health surveys using machine learning,” Current Research in Behavioral Sciences, vol. 7, no. 1, Jan. 2024, doi: 10.1016/j.crbeha.2024.100157.
M. Rahma, M. Fikry, and Y. Afrillia, “Prediksi Kesehatan Mental Remaja Berdasarkan Faktor Lingkungan Sekolah Menggunakan Machine Learning,” Jurnal Informatika: Jurnal Pengembangan IT, vol. 10, no. 2, pp. 382–390, Apr. 2025, doi: 10.30591/jpit.v10i2.8556.
Y. A. Rizky, A. Aziz, and W. Harianto, “Implementasi Naive Bayes Dengan Menggunakan Metode Laplace Smoothing,” RAINSTEK: Jurnal Terapan Sains dan Teknologi, vol. 6, no. 3, pp. 164–172, Sep. 2024, doi: 10.21067/jtst.v6i3.9132.
M. Fadhilla, R. Wandri, A. Hanafiah, P. R. Setiawan, Y. Arta, and S. Daulay, “Analisis Performa Algoritma Machine Learning Untuk Identifikasi Depresi Pada Mahasiswa,” Journal of Informatics Management and Information Technology, vol. 5, no. 1, pp. 40–47, Jan. 2025, doi: 10.47065/jimat.v5i1.473.
F. N. Zahrah and M. Muljono, “Machine Learning untuk Deteksi Stres Pelajar: Perceptron sebagai Model Klasifikasi Efektif untuk Intervensi Dini,” Edumatic: Jurnal Pendidikan Informatika, vol. 8, no. 2, pp. 764–773, Dec. 2024, doi: 10.29408/edumatic.v8i2.28011.
B. Ramadhani and R. R. Suryono, “Komparasi Algoritma Naïve Bayes dan Logistic Regression Untuk Analisis Sentimen Metaverse,” Jurnal Media Informatika Budidarma, vol. 8, no. 2, p. 714, Apr. 2024, doi: 10.30865/mib.v8i2.7458.
M. Sadikin and R. Oktari Batubara, “Comparative Analysis of Algorithm of Naive Bayes and C4.5 on Employee Performance Classification,” Jurnal Elektronik Ilmu Komputer Udayana, vol. 13, no. 2, pp. 309–320, Nov. 2024, doi: https://doi.org/10.24843/JLK.2024.v13.i02.p08.
L. Susanti, “Klasifikasi Tingkat Stress Pada Mahasiswa Teknik Informatika Dalam Melakukan Perkuliahan Metode Hybrid Menggunakan Algoritma Naive Bayes,” STRING (Satuan Tulisan Riset dan Inovasi Teknologi), vol. 8, no. 3, pp. 243–248, Apr. 2024, doi: http://dx.doi.org/10.30998/string.v8i3.17096.
M. F. Alamsyah and A. Wijaya, “Perbandingan Metode KNN dan Naïve Bayes dalam Deteksi Tingkat Stres Berdasarkan Ekspresi Wajah,” Jurnal Informatika: Jurnal Pengembangan IT, vol. 10, no. 2, pp. 359–369, Apr. 2025, doi: 10.30591/jpit.v10i2.8513.
M. Rakha Zulfikar et al., “Hubungan Antara Gangguan Depresi Dan Kualitas Hidup Pada Pasien Hemodialisis Di Rsud Ulin Banjarmasin,” Homeostasis, vol. 7, no. 3, pp. 481–488, Dec. 2024, doi: https://doi.org/10.20527/ht.v7i3.14535.
S. Khoerunnisa, D. F. Shiddieq, and D. Nurhayati, “Penerapan Algoritma Naive Bayes dengan Teknik TF-IDF dan Cross Validation untuk Analisis Sentimen Terhadap Starlink,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 5, no. 2, pp. 566–577, Mar. 2025, doi: 10.57152/malcom.v5i2.1852.
K. Rahayu, V. Fitria, D. Septhya, R. Rahmaddeni, and L. Efrizoni, “Klasifikasi Teks untuk Mendeteksi Depresi dan Kecemasan pada Pengguna Twitter Berbasis Machine Learning,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 3, no. 2, pp. 108–114, Sep. 2023, doi: 10.57152/malcom.v3i2.780.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Deteksi Dini Depresi Mahasiswa Tingkat Akhir Menggunakan Algoritma Naïve Bayes dan Instrumen PHQ-9
Pages: 1252-1264
Copyright (c) 2025 Muhammad Sadikin, Dini Ridha Dwiki Putri, Azrifirizky Amanda
This work is licensed under a Creative Commons Attribution 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 Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment 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 acknowledgment 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 (Refer to The Effect of Open Access).
