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

Qoriani Widayati; Kusworo Adi; R Rizal Isnanto; Eka Puji Agustini; Dewa Rizki Rahmat Julianto; Fawwaz Bimo Prakasa

doi:10.51519/journalisi.v7i1.977

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

DOI: 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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