Predicting Student Loyalty in Higher Education Using Machine Learning: A Random Forest Approach

Authors

  • Qoriani Widayati Universitas Bina Darma, Indonesia
  • Kusworo Adi Diponegoro University, Indonesia
  • R Rizal Isnanto Diponegoro University, Indonesia
  • Eka Puji Agustini Universitas Bina Darma, Indonesia
  • Dewa Rizki Rahmat Julianto Universitas Bina Darma, Indonesia
  • Fawwaz Bimo Prakasa Universitas Bina Darma, Indonesia
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DOI:

https://doi.org/10.51519/journalisi.v7i1.977

Keywords:

student loyalty, random forest, machine learning

Abstract

Student loyalty is a crucial factor supporting the sustainability of higher education institutions. The aim of this study is to predict student loyalty using a machine learning approach, specifically the random forest algorithm. The data for this research were collected through a questionnaire that included variables such as service quality, emotional attachment, brand satisfaction, brand trust, and socio-economic conditions, distributed to 107 students in Palembang. The resulting dataset was processed through preprocessing, model training, and performance evaluation, employing metrics such as accuracy, precision, recall, and F1-score. The analysis using the random forest algorithm achieved an accuracy of 90.9%. These findings are expected to provide valuable insights for higher education institutions in developing more effective strategies to enhance student loyalty.

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Published

2025-03-18

Issue

Vol. 7 No. 1 (2025): March

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Articles

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How to Cite

[1]
Q. Widayati, K. Adi, R. R. Isnanto, E. P. Agustini, D. R. R. Julianto, and F. B. Prakasa, “Predicting Student Loyalty in Higher Education Using Machine Learning: A Random Forest Approach”, journalisi, vol. 7, no. 1, pp. 63–77, Mar. 2025, doi: 10.51519/journalisi.v7i1.977.