Clustering for Motivating New Student Admissions in Study Program Selection: Systematic Literature Review
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Abstract
This research aims to evaluate clustering in new student admissions in determining effective strategies, to help prospective students in choosing study programs that match the interests and potential of prospective new students. Clustering as a machine learning technique to group data that has similarities, is increasingly used in the field of education to support the decision-making process. This Systematic Literature Review (SLR) examines the application of clustering methods in new student admissions, especially in recommending the right study program. By analyzing 10 studies in applying clustering methods that are often used, to determine the main factors that influence the selection of courses, as well as their impact on student satisfaction in choosing courses and optimal academic results. The results of this study provide insight into strategies for the admissions team in optimizing marketing, so that there is a more effective alignment between student profiles and study program characteristics.
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References
Amin, H., & Utomo, W. H. (2023). CLUSTERING ANALYSIS OF ADMISSION OF NEW STUDENTS USING K-MEANS CLUSTERING AND K-MEDOIDS ALGORITHMS TO INCREASE CAMPUS MARKETING POTENTIAL. http://bsti.ubd.ac.id/e-jurnal
Dana, R. D., Rohmat, C. L., & Rinaldi, A. R. (2019). Strategi Marketing Penerimaan Mahasiswa Baru Menggunakan Machine Learning dengan Teknik Clustering. Jurnal Informatika: Jurnal Pengembangan IT, 4(2–2), 201–204. https://doi.org/10.30591/jpit.v4i2-2.1879
Deka, B. K., & Talukdar, C. (2022). Futuristic Trends in Artificial Intelligence MACHINE LEARNING-BASED SELECTION OF PHD ADMISSION MACHINE LEARNING-BASED SELECTION OF PHD ADMISSION MACHINE LEARNING-BASED SELECTION OF PHD ADMISSION. In IIP Proceedings (Vol. 2, Issue 16).
Fattah Mashat, A., Fouad, M. M., Yu, P. S., & Arabia Tarek Gharib, S. F. (2012). Efficient Clustering Technique for University Admission Data. In International Journal of Computer Applications (Vol. 45, Issue 23).
Helmi, F. (2023). Analisis dan Penerapan Algoritma K-Means Clustering Sebagai Strategi Promosi Penerimaan Mahasiswa Baru Pada Universitas Wiraraja. 2(1), 1–11. https://doi.org/10.35316/justify.v2i1.3205
Mamaril, J. C. O., & Ballera, M. A. (2022). Multiple educational data mining approaches to discover patterns in university admissions for program prediction. International Journal of Informatics and Communication Technology (IJ-ICT), 11(1), 45. https://doi.org/10.11591/ijict.v11i1.pp45-56
Muhammad Zulfadhilah, Mambang, & Septyan Eka Prastya. (2022). Implementasi Metode K-Means Clustering untuk Meningkatkan Penjaringan Mahasiswa. TEMATIK, 9(2), 152–160. https://doi.org/10.38204/tematik.v9i2.1053
Suartana, I. M., & Hidayat, A. I. N. (2018). Analysis of New Student Selection using Clustering Algorithms. IOP Conference Series: Materials Science and Engineering, 288(1). https://doi.org/10.1088/1757-899X/288/1/012079
Yusuf, D., Sestri, E., Razi, F., Teknologi dan Bisnis Ahmad Dahlan, I., Ir Juanda No, J. H., & Tangerang Selatan, K. (2024). JIKA | 484 PENGELOMPOKKAN DATA MAHASISWA MENGGUNAKAN CLUSTERING UNTUK OPTIMALISASI PENERIMAAN MAHASISWA BARU. In Jurnal Informatika) Universitas Muhammadiyah Tangerang P (Vol. 8, Issue 4).