Classification of Anxiety Levels in Vocational Students Through Life Story Analysis Using Multi-class SVM
DOI:
https://doi.org/10.22437/teknopedagogi.v15i2.46957Keywords:
Anxiety, Classification, Multi-class Support Vector Machine, Vocational High SchoolAbstract
Anxiety is a psychological condition frequently experienced by vocational high school students due to academic pressure, practical training demands, and uncertainty about future careers. This study aims to (1) classify the anxiety levels of vocational students based on their personal narratives shared via WhatsApp conversations, and (2) compare the performance of two kernel types in the Multi-class Support Vector Machine (SVM) classification model. This quantitative study used a computational experimental design involving 670 Grade X students from a vocational school in Gianyar, Bali. A total of 1,476 narrative texts were collected and labeled into five anxiety levels based on the DASS-42 scale: normal, mild, moderate, severe, and very severe. The classification process applied TF-IDF vectorization and compared the Radial Basis Function (RBF) and Sigmoid kernels. Evaluation results showed that the Sigmoid kernel achieved the highest accuracy (81.42%) and macro-average F1-score (0.7914), demonstrating better performance in recognizing minority classes. The model successfully identified students with severe (10.5%) and very severe (9.1%) anxiety, supporting its potential use for early psychological screening. These findings confirm that Multi-class SVM is effective for classifying anxiety levels from digital narratives and can be integrated into school-based mental health monitoring systems.
Downloads
References
Adhara, H., Erika, E., & Wahyuni, S. (2023). Social anxiety levels among adolescents at social child welfare institutions in Pekanbaru City. International Journal of Nursing and Health Services (IJNHS), 6(4), 246–256. https://doi.org/10.35654/ijnhs.v6i4.691
Aditya, E., Astawa, I. G. K., Limbong, K. G., Indrawan, G., Indrawan, G., & Gunawan, M. A. O. (2025). Analisis sentimen pengguna sistem e-kinerja desa Kabupaten Jembrana menggunakan metode naive bayes. Jurnal Teknologi dan Sistem Informasi Bisnis, 7(1), 8–14. https://doi.org/10.47233/jteksis.v7i1. 1693
Altintaş, E., Uylaş Aksu, Z., & Gümüş Demir, Z. (2021). Machine learning techniques for anxiety disorder. European Journal of Science and Technology, 31(31), 365–374. https://doi.org/10.31590/ejosat. 999914
Angelin, L., Wetik, S. V., & Ferlan, P. A. (2021). Tingkat stres berhubungan dengan kejadian Generalized Anxiety Disorder (GAD) pada mahasiswa tingkat akhir. Jurnal Keperawatan Jiwa, 9(2), 399. https://doi.org/10.26714/jkj.9.2.2021.399-408
Arif, Ashjan, B., Ghufran, M., Taghreed, S., Nada, A., Nada, A., & Maryam, A. (2020). Classification of anxiety disorders using machine learning methods: A literature review. Insights of Biomedical Research, 4(1). https://doi.org/10.36959/584/455
Beka-Dede, E. V., Mola, S. A. S., & Nabuasa, Y. Y. (2022). Implementasi hamilton anxiety rating scale untuk mendiagnosis tingkat kecemasan pada mahasiswa Dalam Penyusunan Skripsi. Jurnal Komputer dan Informatika, 10(1), 55–64. https://doi.org/10.35508/jicon.v10i1.6353
Bustomi, A. R., & Yuliana, N. (2023). Peran aplikasi whatsapp dalam dinamika ilmu komunikasi. Triwikrama: Jurnal Multidisiplin Ilmu Sosial, 2(4), 2023–2054
Damayanti, P. S., Putra, A., & Srirahmawati, I. (2021). Pengembangan kecerdasan emosional melalui pendidikan karakter pada peserta didik di Sekolah Dasar. Equilibrium: Jurnal Pendidikan, 9(3), 348–356. https://doi.org/10.26618/equilibrium.v9i3.5992
Demartoto, A., Sunesti, Y., Haryono, B., & Mundayat, A. A. (2020). Life story of patient with supervision’s fighting against Covid-19 in Surakarta Indonesia. Jurnal Analisa Sosiologi, 9(2), 423–435. https://doi.org/10.20961/jas.v9i2.44436
Handayani, R. N. (2021). Optimasi algoritma support vector machine untuk analisis sentimen pada ulasan produk tokopedia menggunakan PSO. Media Informatika, 20(2), 97–108. https://doi.org/10.37595/ mediainfo.v20i2.59
Handayuni, T. S., & Ifdil, I. (2020). The concept of anxiety in practice exam among vocational school students. Education and Social Sciences Review, 1(1), 23. https://doi.org/10.29210/07essr48000
Hatta, H., & Zia Ulhaq, M. (2022). Penggunaan media sosial whatsapp di kalangan mahasiswa program studi desain komunikasi visual Universitas Negeri Makassar. Jurnal Kependidikan Media, 11(3), 153–161. https://doi.org/10.26618/jkm.v11i3.9492
Hendrayana, I. G., Divayana, D. G. H., & Kesiman, M. W. A. (2023). Komparasi Metode Svm, K-Nn dan Nbc pada analisis sentimen. Jurnal Indonesia: Manajemen Informatika dan Komunikasi, 4(1), 191–198. https://doi.org/10.35870/jimik.v4i1.157
Idris, I. S. K., Mustofa, Y. A., & Salihi, I. A. (2023). Analisis sentimen terhadap penggunaan aplikasi shopee mengunakan Algoritma Support Vector Machine (SVM). Jambura Journal of Electrical and Electronics Engineering, 5(1), 32–35. https://doi.org/10.37905/jjeee.v5i1.16830
Indrawan, G., Setiawan, H., & Gunadi, A. (2022). Multi-class SVM classification comparison for health service satisfaction survey data in bahasa. HighTech and Innovation Journal, 3(4), 425–442. https://doi.org/10.28991/HIJ-2022-03-04-05
Isnaini, Z., Supriyono, A., & Rachma, S. N. (2023). Efektifitas penggunaan media sosial whatsapp dalam komunikasi kelompok. MASSIVE: Jurnal Ilmu Komunikasi, 3(1), 15. https://doi.org/10.35842/ massive.v3i1.84
Jayadi, B. V., Handhayani, T., & Lauro, M. D. (2023). Perbandingan KNN dan SVM untuk klasifikasi kualitas udara di Jakarta. Jurnal Ilmu Komputer dan Sistem Informasi, 11(2). https://doi.org/10.24912/jiksi.v11i2.26006
Kurnia, K., & Hakim, D. L. (2023). Identification of career choice for vocational high school students: A review. Jiptek, 16(2), 150. https://doi.org/10.20961/jiptek.v16i2.67732
Lumban-Gaol, N. T. (2016). Teori stres: Stimulus, respons, dan transaksional. Buletin Psikologi, 24(1), 1. https://doi.org/10.22146/bpsi.11224
Manuaba, I. B. N. W., Dantes, G. R., & Indrawan, G. (2022). Analisis sentimen data provider layanan internet pada twitter menggunakan support vector machine dengan penambahan algoritma levenshtein distance. Jurnal SISKOM-KB (Sistem Komputer Dan Kecerdasan Buatan), 5(2), 9–17. https://doi.org/10.47970/siskom-kb.v5i2.261
Muhammad, A. D., Rosyidawati, N. H., Sudrajat, A. A., Khairunnisa, N. H., Rahmawati, B. D. Z., Khatimah, W. H., Apriyani, P., Andriani, A. P. D., Widyastuti, P. A., Suryani, D. S., Nur Azizah, P. F. S., & Yuniasih, D. (2021). Anxiety of final semester students: Mini review. Ahmad Dahlan Medical Journal, 2(2), 85–92. https://doi.org/10.12928/admj.v2i2.5432
Muqorobin, M., & Triana, H. (2022). Validitas instrumen tingkat kecemasan siswa terhadap pembelajaran matematika dalam penelitian eksperimen. JISIP (Jurnal Ilmu Sosial Dan Pendidikan), 6(2), 4173–4181. https://doi.org/10.58258/jisip.v6i2.3121
Oktamarin, L., Kurniati, F., Sholekhah, M., Nurjanah, S., Oktaria, S. W., Sukmawati, S., & Apriyani, T. (2022). Gangguan kecemasan (axiety disorder) pada anak usia dini. Jurnal Multidisipliner Bharasumba, 1(02), 119–134. https://doi.org/10.62668/bharasumba.v1i02.192
Paramartha, A., Mudjihartono, P., & Emanuel, A. W. R. (2022). Analysis of emotional aspect of twitter postings about large scale social limitations in suppressing the spread of Covid-19. International Journal of Information Science and Management, 20(2), 1–14. https://doi.org/20.1001.1.20088302. 2022.20.2.1.6
Prasetyowati, D., Indiati, I., & Nayla, A. (2021). Analisis keterlaksanaan perencanaan dan proses kegiatan pembelajaran praktik di SMK selama pandemic Covid 19. Jurnal Riptek, 15(2), 69–74. https://doi.org/10.35475/riptek.v15i2.121
Purnama, G. W., Permana, A. A. J., Ananda, K. N., Purnami, N. L. I., Nugraha, G. N. A., & Yogi, I. B. S. M. (2024). Implementasi sistem pakar untuk klasifikasi tanaman padi (Oryza sativa L.). Jurnal Pendidikan Teknik Elektro, 13(2)(11), 171–185. https://doi.org/10.23887
Pusparani, A. D., Kesiman, M. W. A., & Aryanto, Y. E. K. (2025). Identification of little tuna species using Convolutional Neural Networks (CNN) Method and ResNet-50 Architecture. Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM), 8(1), 86–93. https://doi.org/10.24014/ijaidm.v8i1. 31620
Putra, D. P., & Wardijono, B. A. (2020). Analisis akurasi penerapan algoritma support vector machine menggunakan kernel radial basis function pada penentuan kelayakan kredit (studi kasus german kredit data). Jurnal Ilmiliah KOMPUTASI, 19(2), 175–180. http://dx.doi.org/10.32409/jikstik.19.2.2786
Putri, I. R., Setiawan, A., & Nasrudin, D. (2022). Energy literacy profile of vocational high school teacher candidates for renewable energy engineering expertise program. Jurnal Ilmiah Pendidikan Teknik Dan Kejuruan, 15(2), 99. https://doi.org/10.20961/jiptek.v15i2.67623
Rabbani, S., Safitri, D., Rahmadhani, N., Sani, A. A. F., & Anam, M. K. (2023). Perbandingan evaluasi kernel SVM untuk klasifikasi sentimen dalam analisis kenaikan harga BBM. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 3(2), 153–160. https://doi.org/10.57152/ malcom.v3i2.897
Rachmawati, A. D., & Kusumah, Y. S. (2023). Evaluation of industrial work practice program in Vocational High School 1 Rejotangan. Jurnal Pendidikan Vokasi, 13(1), 60–68. https://doi.org/10.21831/jpv. v13i1.46917
Rahmadhani, S., Ahyanuardi, & Suryati, L. (2022). Vocational high school students’ competency needs to the world of work. Mimbar Ilmu, 27(2), 349–355. https://doi.org/10.23887/mi.v27i1.42161
Sasmita, A., Pradnyana, G. A., & Divayana, D. G. H. (2022). Pengembangan sistem analisis sentimen untuk evaluasi kinerja Dosen Universitas Pendidikan Ganesha dengan Metode Naïve Bayes. JST (Jurnal Sains Dan Teknologi), 11(2), 451–462. https://doi.org/10.23887/jstundiksha.v11i2.44384
Setiawan, H., Gunadi, A., & Indrawan. (2023). Klasifikasi pelayanan kesehatan berdasarkan data sentimen pelayanan kesehatan menggunakan Multiclass Support Vector Machine. Jurnal Sistem dan Informatika (JSI), 17(1), 47–54. https://doi.org/10.30864/jsi.v17i1.512
Sheykhmousa, M., Mahdianpari, M., Ghanbari, H., Mohammadimanesh, F., Ghamisi, P., & Homayouni, S. (2020). Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 6308–6325. https://doi.org/10.1109/JSTARS.2020.3026724
Soen, C. C., Hardjasasmita, I. M., & Ulitua, A. E. (2022). Generalized anxiety disorder: Diagnosis and treatment. Jurnal Muara Medika Dan Psikologi Klinis, 1(2), 133. https://doi.org/10.24912/jmmpk. v1i2.14865
Susatya, E., Triatmaja, A. K., Suratno, E., & Faisyal Fachri, F. (2023). Impact of the center of excellence vocational High School Program on the interest of new learners. Journal of Vocational Education Studies, 6(1), 62–75. https://doi.org/10.12928/joves.v6i1.7256
Syahira, M. A., & Kurniawan, R. (2024). Analisis sentimen cyberbullying pada media sosial X menggunakan Metode Support Vector Machine. Jurnal Media Informatika Budidarma, 8(3), 1724. https://doi.org/10.30865/mib.v8i3.7926
Wibawa, I. P. G. S. T., Sariyasa, & Suarni, N. K. (2020). Pengaruh model pembelajaran inkuiri berbasis STEM dengan penugasan berbantuan Aplikasi Whatsaap terhadap partisipasi orang tua dan hasil belajar IPA siswa kelas IV SD se-gugus VIII mengwi. Jurnal Pendidikan Dasar Indonesia, 4(2), 79–90. https://ejournal-pasca.undiksha.ac.id/index.php/jurnal_pendas/article/view/3319/1786
Widarto, W., Nawanksari, S., & Utami, P. M. (2024). The urgency of interpersonal skills, leadership and self-discipline for vocational high school students majoring in aesthetics and hairdressing. Jurnal Pendidikan Dan Pengajaran, 57(3), 520–531. https://doi.org/10.23887/jpp.v57i3.77440
Widhiyanti, A. A. S., Candiasa, I. M., & Aryanto, K. Y. E. (2021). Implementasi AHP-Topsis dan naïve bayes dalam sistem pendukung keputusan pemberian bimbingan konseling siswa. SINTECH (Science and Information Technology) Journal, 4(2), 129–138. https://doi.org/10.31598/ sintechjournal.v4i2.731
Yolanda, A. M., & Mulya, R. T. (2024). Implementasi metode support vector machine untuk analisis sentimen pada ulasan Aplikasi Sayurbox di Google Play Store. VARIANSI: Journal of Statistics and Its Application on Teaching and Research, 6(2), 76–83. https://doi.org/10.35580/variansiunm258
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Ni Putu Dea Sillviari, I Made Candiasa, Gede Indrawan
This work is licensed under a Creative Commons Attribution 4.0 International License.
