Optimizing University Admissions: A Machine Learning Perspective

Authors

  • Aga Maulana Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Teuku Rizky Noviandy Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Novi Reandy Sasmita Computational and Applied Statistics Research Group, Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Maria Paristiowati Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Negeri Jakarta, Jakarta 13220, Indonesia
  • Rivansyah Suhendra Department of Information Technology, Faculty of Engineering, Universitas Teuku Umar, Aceh Barat 23681, Indonesia
  • Erkata Yandri Graduate School of Renewable Energy, Darma Persada University, Jl. Radin Inten 2, Pondok Kelapa, East Jakarta 13450, Indonesia
  • Justinus Satrio Department of Chemical Engineering, Villanova University, Villanova 19085, United States
  • Rinaldi Idroes School of Mathematics and Applied Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia

DOI:

https://doi.org/10.60084/jeml.v1i1.46

Keywords:

Educational management, Machine learning, University admission, Prediction model

Abstract

The university admission process plays a pivotal role in shaping the future of aspiring students. However, traditional methods of admission decisions often fall short in capturing the holistic capabilities of individuals and may introduce bias. This study aims to improve the admission process by developing and evaluating machine learning approach to predict the likelihood of university admission. Using a dataset of previous applicants' information, advanced algorithms such as K-Nearest Neighbors, Random Forest, Support Vector Regression, and XGBoost are employed. These algorithms are applied, and their performance is compared to determine the best model to predict university admission. Among the models evaluated, the Random Forest algorithm emerged as the most reliable and effective in predicting admission outcomes. Through comprehensive analysis and evaluation, the Random Forest model demonstrated its superior performance, consistency, and dependability. The results show the importance of variables such as academic performance and provide insights into the accuracy and reliability of the model. This research has the potential to empower aspiring applicants and bring positive changes to the university admission process.

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Published

2023-06-27

How to Cite

Maulana, A., Noviandy, T. R., Sasmita, N. R., Paristiowati, M., Suhendra, R., Yandri, E., Satrio, J., & Idroes, R. (2023). Optimizing University Admissions: A Machine Learning Perspective. Journal of Educational Management and Learning, 1(1), 1–7. https://doi.org/10.60084/jeml.v1i1.46

Issue

Vol. 1 No. 1 (2023): August 2023

Section

Articles

License

Copyright (c) 2023 Aga Maulana, Teuku Rizky Noviandy, Novi Reandy Sasmita, Maria Paristiowati, Rivansyah Suhendra, Erkata Yandri, Justinus Satrio, Rinaldi Idroes

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.