Klasifikasi Multilabel Artikel Academic Burnout Berbasis Study Demands-Resources Menggunakan TF–IDF dan Linear SVM
Keywords:
Academic Burnout, Klasifikasi Multilabel, Study Demands-Resources, TF–IDF, Linear SVMAbstract
Literatur academic burnout mahasiswa membahas tuntutan akademik, dukungan lingkungan belajar, dan kapasitas psikologis individu yang sering muncul secara bersamaan dalam satu penelitian. Kerangka Study Demands-Resources (SD-R) digunakan karena memberikan pembedaan konseptual antara tuntutan yang berpotensi menguras energi, sumber daya eksternal yang mendukung proses belajar, dan sumber daya personal yang menunjang kemampuan adaptasi mahasiswa. Pembedaan tersebut memungkinkan pengelompokan artikel berdasarkan peran konstruk yang dianalisis, bukan sekadar kategori faktor internal dan eksternal. Penelitian ini bertujuan mengevaluasi penerapan Term Frequency-Inverse Document Frequency (TF–IDF) dan Linear Support Vector Machine (Linear SVM) untuk mengklasifikasikan artikel academic burnout secara multilabel ke dalam Study Demands (D), Study Resources (R), dan Personal Resources (P). Eksperimen menggunakan 113 artikel berlabel, terdiri atas 90 data latih dan 23 data uji. Hasil evaluasi menunjukkan macro F1 sebesar 73,56% dan micro F1 sebesar 75,29%, dengan F1 masing-masing sebesar 78,57% pada D, 63,16% pada R, dan 78,95% pada P. Meskipun P memperoleh skor F1 tertinggi, model belum mampu mengidentifikasi kasus negatif pada kategori tersebut. Temuan ini menunjukkan bahwa pendekatan multilabel dapat mendukung pengelompokan literatur berdasarkan konstruk SD-R, tetapi kinerja antarkategori belum merata. Penelitian ini berkontribusi pada pengembangan pendekatan komputasional untuk penelusuran literatur academic burnout, dengan keterbatasan pada ukuran data uji, pemilihan sampel secara terarah, dan konsistensi anotasi.
References
Bakker, A. B., & Demerouti, E. (2017). Job demands–resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273–285. https://doi.org/10.1037/ocp0000056
Bolatov, A. K., Seisembekov, T. Z., Smailova, D. S., & Hosseini, H. (2022). Burnout syndrome among medical students in Kazakhstan. BMC Psychol, 10(1), 193. https://doi.org/10.1186/s40359-022-00901-w
Chen, J., & Chen, G. (2025). Academic burnout among Chinese college students: A study based on FSQCA method. Acta Psychologica, 253. https://doi.org/10.1016/j.actpsy.2025.104701
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297. https://doi.org/10.1007/BF00994018 Cuevas-Caravaca, E., Sánchez-Romero, E. I., & Antón-Ruiz, J. A. (2024). Academic Burnout, Personality, and Academic Variables
in University Students. Eur J Investig Health Psychol Educ, 14(6), 1561–1571. https://doi.org/10.3390/ejihpe14060103 Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demands-resources model of burnout. Journal of
Applied Psychology, 86(3), 499–512. https://doi.org/10.1037/0021-9010.86.3.499
Gu, J., Wu, P., Luo, Y., He, X., Fu, L., Liu, H., Lin, F., Xu, Q., & Wu, X. (2023). Internet addiction, loneliness, and academic burnout among Chinese college students: a mediation model. Front Psychiatry, 14, 1176596. https://doi.org/10.3389/fpsyt.2023.1176596
Javed, M. Q., Ahmad, Z., Muhammad, M., Binrayes, A., Niazi, I., Nawabi, S., Abulhamael, A. M., & Habib, S. R. (2024). Burnout level evaluation of undergraduate dental college students at middle eastern university. BMC Med Educ, 24(1), 1155. https://doi.org/10.1186/s12909-024-06149-9
Jeon, M. K., Lee, I., & Lee, M. Y. (2022). The multiple mediating effects of grit and learning agility on academic burnout and learning engagement among Korean university students: a cross-sectional study. Ann Med, 54(1), 2710–2724. https://doi.org/10.1080/07853890.2022.2122551
Jeon, M., Lee, I., & Lee, M. (2022). The multiple mediating effects of grit and learning agility on academic burnout and learning engagement among Korean university students: a cross-sectional study. Annals of Medicine. https://doi.org/10.1080/07853890.2022.2122551
Lesener, T., Pleiss, L. S., Gusy, B., & Wolter, C. (2020). The Study Demands-Resources Framework: An Empirical Introduction. Int J Environ Res Public Health, 17(14), 5183. https://doi.org/10.3390/ijerph17145183
Liu, Y., Zhang, Y., Peng, C., Li, Y., & Tan, Q. (2023a). Cumulative Ecological Risk and Academic Burnout in Chinese College Students: A Moderated Mediation Model. International Journal of Environmental Research and Public Health, 20(3). https://doi.org/10.3390/ijerph20031712
Liu, Y., Zhang, Y., Peng, C., Li, Y., & Tan, Q. (2023b). Cumulative Ecological Risk and Academic Burnout in Chinese College Students: A Moderated Mediation Model. International Journal of Environmental Research and Public Health. https://doi.org/10.3390/ijerph20031712
Marôco, J., Assunção, H., Harju-Luukkainen, H., Lin, S. W., Sit, P. S., Cheung, K. C., Maloa, B., Ilic, I. S., Smith, T. J., & Campos,
J. (2020). Predictors of academic efficacy and dropout intention in university students: Can engagement suppress burnout?
PLoS One. https://doi.org/10.1371/journal.pone.0239816
Olson, N., Oberhoffer-Fritz, R., Reiner, B., & Schulz, T. (2023). Study related factors associated with study engagement and student burnout among German university students. Front Public Health, 11, 1168264. https://doi.org/10.3389/fpubh.2023.1168264
Önder, ?., Önder, A. N., & Güven Y?ld?r?m, E. (2025). Morningness and academic burnout among university students: the mediating role of procrastination. BMC Psychol, 13(1), 630. https://doi.org/10.1186/s40359-025-02969-6
Rahmatpour, P., Chehrzad, M., Ghanbari, A., & Sadat-Ebrahimi, S. R. (2019). Academic burnout as an educational complication and promotion barrier among undergraduate students: A cross-sectional study. J Educ Health Promot, 8, 201. https://doi.org/10.4103/jehp.jehp_165_19
Salmela-Aro, K., Upadyaya, K., Ronkainen, I., & Hietajärvi, L. (2022). Study Burnout and Engagement During COVID-19 Among University Students: The Role of Demands, Resources, and Psychological Needs. J Happiness Stud, 23(6), 2685–2702. https://doi.org/10.1007/s10902-022-00518-1
Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513–523. https://doi.org/10.1016/0306-4573(88)90021-0
Sanjari, E., Majidian Dehkordi, F., & Raeisi Shahraki, H. (2023). Clustering Undergraduate Students Based on Academic Burnout and Satisfaction from the Field Using Partitioning around Medoid. Comput Math Methods Med, 2023, 8898939. https://doi.org/10.1155/2023/8898939
Song, W., Wang, Z., & Zhang, R. (2022). Classroom Digital Teaching and College Students’ Academic Burnout in the Post COVID-19 Era: A Cross-Sectional Study. International Journal of Environmental Research and Public Health, 19(20). https://doi.org/10.3390/ijerph192013403
Ye, Y., Huang, X., & Liu, Y. (2021). Social support and academic burnout among university students: A moderated mediation model. Psychology Research and Behavior Management, 14, 335–344. https://doi.org/10.2147/PRBM.S300797
