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Abstract

Stress among university students in Lampung Province has become a significant concern. This study aims to delve deeper into the unique and context-specific risk factors associated withstress by leveraging the decision tree algorithm. Through in-depth analysis of data encompassing academic pressure, social support,extracurricular involvement, adaptability, sleep patterns, the impact of campus green spaces, and spirituality, this research endeavors to develop an accurate predictive model. Consequently, it is expected to identify distinctive stress patterns and provide targeted intervention recommendations, such as data-driven counseling programs, the development of peer support communities, or the optimization of academic counseling services. The findings of this study are anticipated to make a substantial contribution to improving the mental well-being of students in Lampung Province and serve as a reference for similar studies in other regions.

Keywords

Risk Factors Stress Students Decision Tree

Article Details

References

  1. Auerbach, R. P. . M. P. . B. R. . A. J. . B. C. . C. P. . D. K. . E. D. . G. J. . H. P. . M. R. . N. M. K. ., & Pinder-Amaker, S. . S. N. A. . V. G. . Z. A. M. . K. R. C. . W. W.-I. C. (2018). t: Prevalence and distribution of mental disorder. WHO World Mental Health Surveys International College Student Project.
  2. Prasetio, C. E., & Triwahyuni, A. (2022). Gangguan Psikologis pada Mahasiswa Jenjang Sarjana: Faktor-Faktor Risiko dan Protektif. Gadjah Mada Journal of Psychology (GamaJoP), 8(1), 56. https://doi.org/10.22146/gamajop.68205
  3. Ahmad Saleh, 2015, Klasifikasi Gejala Depresi Pada Manusia dengan Metode Naïve Bayes Menggunakan Java, Yogyakarta.
  4. Boone, Louis E dan Kurtz, David L. 2010. Pengantar Bisnis Kontemporer. Terjemahan Anwar Fadriansyah. Jakarta: Erlangga.
  5. Davies. B., “Database Systems 3rd Edition”, Basingstoke, UK: Palgrave, 2004.
  6. Griffin, Ricky W., and Moorhead, Gregory., 2014. Organizational Behavior: Managing People and Organizations. Eleventh Edition. USA: South Western.
  7. Han, J. dan M. Kamber. 2006. Data Mining Concepts and Techniques Second Edition. San Francisco: Morgan Kaufmann.
  8. Hermawati. 2013. Data Mining. Yogyakarta: Penerbit ANDI, Jl. Beo.
  9. Ian H. Witten, E. F. (2005). Data mining: Practical Machine Learning Tools and Techniques, Second Edition. San Francisco: Elsevier Inc.
  10. Jananto, A. 2013. Algoritma Naive Bayes untuk Mencari Perkiraan Waktu Studi Mahasiswa. Teknologi Informasi DINAMIK, Vol.18 No.1, 9–16.
  11. Laksana, Fajar. 2008. Manajemen Pemasaran. Edisi 1. Yogyakarta: Grama Ilmu
  12. Moleong, Lexy J. (2007) Metodologi Penelitian Kualitatif, Penerbit PT Remaja Rosdakarya Offset, Bandung
  13. Murtopo, A. A. (2015). Prediksi Kelulusan Tepat Waktu Mahasiswa STMIK YMI Tegal Menggunakan Algoritma Naïve Bayes. CSRID Journal, 7(3), 145–154.
  14. Pramudiono. I., (26 Mei 2017), “Pengantar Data mining: Menambang Permata Penxgetahuan di Gunung Data.” [online], available: http://www.ilmu computer.org/wp-content/uploads/ 2006/08/iko-datamining.
  15. Auerbach, R. P. . M. P. . B. R. . A. J. . B. C. . C. P. . D. K. . E. D. . G. J. . H. P. . M. R. . N. M. K. ., & Pinder-Amaker, S. . S. N. A. . V. G. . Z. A. M. . K. R. C. . W. W.-I. C. (2018). t: Prevalence and distribution of mental disorder. WHO World Mental Health Surveys International College Student Project.
  16. Prasetio, C. E., & Triwahyuni, A. (2022). Gangguan Psikologis pada Mahasiswa Jenjang Sarjana: Faktor-Faktor Risiko dan Protektif. Gadjah Mada Journal of Psychology (GamaJoP), 8(1), 56. https://doi.org/10.22146/gamajop.68205
  17. Ahmad Saleh, 2015, Klasifikasi Gejala Depresi Pada Manusia dengan Metode Naïve Bayes Menggunakan Java, Yogyakarta.
  18. Boone, Louis E dan Kurtz, David L. 2010. Pengantar Bisnis Kontemporer. Terjemahan Anwar Fadriansyah. Jakarta: Erlangga.
  19. Davies. B., “Database Systems 3rd Edition”, Basingstoke, UK: Palgrave, 2004.
  20. Griffin, Ricky W., and Moorhead, Gregory., 2014. Organizational Behavior: Managing People and Organizations. Eleventh Edition. USA: South Western.
  21. Han, J. dan M. Kamber. 2006. Data Mining Concepts and Techniques Second Edition. San Francisco: Morgan Kaufmann.
  22. Hermawati. 2013. Data Mining. Yogyakarta: Penerbit ANDI, Jl. Beo.
  23. Ian H. Witten, E. F. (2005). Data mining: Practical Machine Learning Tools and Techniques, Second Edition. San Francisco: Elsevier Inc.
  24. Jananto, A. 2013. Algoritma Naive Bayes untuk Mencari Perkiraan Waktu Studi Mahasiswa. Teknologi Informasi DINAMIK, Vol.18 No.1, 9–16.
  25. Laksana, Fajar. 2008. Manajemen Pemasaran. Edisi 1. Yogyakarta: Grama Ilmu
  26. Moleong, Lexy J. (2007) Metodologi Penelitian Kualitatif, Penerbit PT Remaja Rosdakarya Offset, Bandung
  27. Murtopo, A. A. (2015). Prediksi Kelulusan Tepat Waktu Mahasiswa STMIK YMI Tegal Menggunakan Algoritma Naïve Bayes. CSRID Journal, 7(3), 145–154.
  28. Pramudiono. I., (26 Mei 2017), “Pengantar Data mining: Menambang Permata Penxgetahuan di Gunung Data.” [online], available: http://www.ilmu computer.org/wp-content/uploads/ 2006/08/iko-datamining.
  29. Pratiwi. 2013. Buku Ajar Sistem Pendukung Keputusan. Jakarta: Penerbit, Media.
  30. Ridwan, M., Suyono, H., & Sarosa, M. (2013). Penerapan Data Mining Untuk Evaluasi Kinerja Akademik Mahasiswa Menggunakan Algoritma Naive Bayes Classifier. Eeccis, 7(1), 59–64.
  31. Santoso. Heroe, Hariyadi. I Putu, Prayitno, “Data Mining Analisa Pola Pembelian Produk Dengan Menggunakan Metode Algoritma Apriori”, 2016
  32. Sugiyono.2017. Metode Penelitian Kuantitatif, Kualitatif dan R&D. Bandung: Penerbit Alfabeta.
  33. Tan, Pang Ning., Michael Steinbach, Vipin Kumar (2004). Introduction to Data Mining.