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All Journal Media Statistika Ekonomi Bisnis Animal Production : Indonesian Journal of Animal Production Conference SENATIK STT Adisutjipto Yogyakarta Bisma: Jurnal Bisnis dan Manajemen Legal Standing : Jurnal Ilmu Hukum UPEJ Unnes Physics Education Journal eBA Journal: Journal Economic, Bussines and Accounting Diklat Review : Jurnal manajemen pendidikan dan pelatihan Jurnal Pendidikan dan Teknologi Indonesia Jurnal Abdi Panca Marga ANGON: Journal of Animal Science and Technology Tepak Sirih : Jurnal Pengabdian Kepada Masyarakat Madani Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Jurnal Informatika Medis (J-INFORMED) IJENSET
Agus Priyono
Bagian Klinik Hewan, Fakultas Kedokteran Hewan, Universitas Udayana, Bali
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A Data-Driven Machine Learning for Predicting Student Stress Levels Purwati, Neni; Priyono, Agus; Rizky Al'insani, Adam; Darmawan
Indonesian Journal of Engineering, Science and Technology Vol. 2 No. 2 (2025): VOL. 02 NO. 02 (DECEMBER 2025)
Publisher : Universitas Muhammadiyah Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38040/ijenset.v2i2.1380

Abstract

Academic stress significantly impacts students' psychological well-being and academic performance. This study focuses on predicting students' stress levels using a data-driven machine learning framework. The dataset was obtained from a questionnaire comprising 25 indicators encompassing emotional, psychological, academic, and environmental aspects of students. The research procedure involved data preprocessing, checking for missing values and redundancy, normalization, descriptive statistical analysis, model development, and performance evaluation using metrics such as recall, precision, sensitivity, specificity, F-measure, and accuracy. The implemented algorithm achieved excellent results, with an overall accuracy of 0.98. The model demonstrated high effectiveness in classifying Eustress and Distress, while its performance in detecting the No Stress category was limited, although precision and specificity indicate a strong capacity to differentiate between classes. These findings confirm that a machine learning approach can effectively capture patterns of student stress based on questionnaire responses and offers valuable guidance for developing early warning systems and targeted psychological intervention strategies. The study highlights the potential of data-driven predictive methods in supporting students' mental health through empirical data analysis. Keywords - LibSVM; Machine Learning; Predicting; Stress Levels; Tree Ensemble.
Co-Authors Abdul Muhsyi aditya permana putra Aditya, Dedy Yusuf Afif Nur Faozi Agustinah Setyaningrum, Agustinah Alif Mirzania Alvinsyah, Dwi Putra Bakar, Abdoel Bambang Susanto Darmawan Destiana, Rea Dewi Prihatini Dian Ekowati Didit Budi Nugroho Gagal Rencana, Feri Susilo Hari Sukarno Hermin Purwaningsih Hidayah, Septina Alfiani Hilmansyah, Muhammad Al Faquri Arief Indra Indra Isa Akhlis Jadidyah, Havida Khanifatul Khusna Krismiwati Muatip Kusmayadi, Kusmayadi Lucie Setiana Marni Astuti, Marni Mochamad Sugiarto Muhammad Nuskhi Muhammad Shodiq N. Ari Subagio Neni Purwati Novi Puspitasari Pambudi Yuwono Pusalia, Retno Ramadhan, Divan Janatra Ramanda, Febri Rizky Al'insani, Adam Rosita, Andriani Salma Fauziyyah Satrio, Aldilla Jalu Socheh, Mochamad Subagio, Ari Subagio, N Ari Uyuunul Mauidzoh Wahyu Hardyanto Yovitantri, Maricia Yoyo Yoyo