Analysis of Factors Influencing World University Rankings Based on Webometrics Indicators Using the Support Vector Machine (SVM) Method
Keywords:
Support Vector Machine (SVM), Determining Factors, University Ranking, Webometrics, Data Analysis, Machine LearningAbstract
University rankings have become an essential indicator in assessing the quality and reputation of higher education institutions worldwide. Webometrics, as one of the leading international university ranking organizations, evaluates institutions based on three main indicators: Impact (50%), Openness (10%), and Excellence (40%). This study aims to analyze the factors that influence university rankings by applying a machine learning approach, specifically the Support Vector Machine (SVM) method. The dataset used in this research was obtained from the Mendeley Data Repository, containing information on 1400 universities across various countries and including Webometrics indicators for the year 2025. The research process involved several stages, including data preprocessing, feature extraction, and modeling using SVM to classify universities into three ranking categories: Top, Middle, and Low. The results of this study are expected to identify the most dominant indicators affecting university ranking positions and to provide strategic recommendations for higher education institutions to enhance their academic performance and global visibility.
References
J. L. Ortega, "Visualizing the Webometrics Ranking of World Universities: A Network Analysis of Institutional Web Presence," Journal of Informetrics, vol. 18, no. 1, pp. 112–125, Feb. 2024, doi: 10.1016/j.joi.2023.101456.
M. A. Al-Nasser and H. Al-Shorbagi, "The Role of Digital Presence in Enhancing Global Academic Competitiveness," International Journal of Information Management, vol. 72, art. no. 102651, 2025, doi: 10.1016/j.ijinfomgt.2024.102651.
R. K. Sahoo and S. S. Mohapatra, "Applying Support Vector Machine for Performance Classification in Educational Data Mining," in Proceedings of the 2024 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2024, pp. 215–221, doi: 10.1109/DSAA61234.2024.00032.
T. Nguyen and P. Vo, "Analyzing the Impact of Open Access Publications on University Rankings Using Machine Learning," Journal of Scholarly Publishing, vol. 55, no. 4, pp. 289–308, Oct. 2024, doi: 10.3138/jsp-2023-0045.
S. Kurniawan and D. P. Hasmoro, "Faktor-Faktor Dominan dalam Penilaian Reputasi Perguruan Tinggi Berbasis Digital," Jurnal Teknologi Informasi dan Pendidikan, vol. 18, no. 2, pp. 77–89, 2025, doi: 10.21831/jti.v18i2.65432.
R. Sarwar, A. Zia, R. Nawaz, A. Fayoumi, N. R. Aljohani, and S.-U. Hassan, “Webometrics: evolution of social media presence of universities,” Scientometrics, vol. 126, no. 2, pp. 951–967, Apr. 2021, doi: 10.1007/s11192-020-03804-y.
L. J. Wardley, E. Rajabi, S. H. Amin, and M. Ramesh, “A machine learning approach feature to forecast the future performance of the universities in Canada,” Machine Learning Applications, vol. 16, art. no. 100548, 2024, doi: 10.1016/j.mlwa.2024.100548.
C. S. Basireddy, V. K. G. Cheruku, B. P., S. Rajagopal, and R. Soangra, “Hybrid prediction models for assessing the higher education competitiveness,” F1000Research, vol. 13, art. 1529, Dec. 2024, doi: 10.12688/f1000research.155847.1.
K. Wisaeng and B. Muangmeesri, “University Rankings Prediction Using Hybrid Feature Selection Based on Machine Learning Methods,” International Journal of Analytical and Applied Mathematics, vol. 23, no. 2, pp. 311–320, 2025, doi: 10.28924/2291-8639-23-2025-112.
B. Tóth, H. Motahari-Nezhad, N. Horseman, L. Berek, L. Kovács, Á. Hölgyesi, and M. Péntek, “Ranking resilience: assessing the impact of scientific performance and expansion of global university rankings on Central European universities,” Scientometrics, vol. 129, no.3, pp. 1739–1770, 2024, doi: 10.1007/s11192-023-04920-1.
F. E. Arévalo-Cordovilla and M. Peña, “Comparative analysis of machine learning models for predicting student success in online programming courses,” Mathematics, vol. 12, no. 20, art. 3272, Oct. 2024, doi: 10.3390/math12203272.
J. H. Guanin-Fajardo, J. Guaña-Moya, and J. Casillas, “Predicting academic success of college students using machine learning techniques,” Data, vol. 9, no. 4, art. 60, Apr. 2024, doi: 10.3390/data9040060.
A. C. Estrada-Real, P. J. Lopez, and L. S. Rojas, “Data analytics approach for university competitiveness and digital visibility,” Journal of Educational Data Science, vol. 6, no. 1, pp. 45–60, 2023, doi: 10.1016/j.jeds.2023.01.006.
D. N. Ayu Ofta Sari, Muhammad Iqbal, “Analisis Faktor Demografi Dan Sosial Ekonomi Untuk Mendeteksi Dini Risiko Putus Kuliah Menggunakan Metode Support Vector Machine (Svm) Dan Decision Tree (Studi Kasus : STMIK Triguna Dharma),” vol. 07, no. 02, p. 17, 2025.
M. Iqbal, S. Pardingotan Sipayung, A. R. Sinaga, and P. M. Hasugian, “Analysis of Student Achievement with K-Means on Socioeconomic, Behavioral, and Psychological Factors,” Inform. dan Sains, vol. 14, no. 04, pp. 715–728, 2024, doi: 10.54209/infosains.v14i04.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Sukrianto, Muhammad Iqbal
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
International Conference On Islamic Community Studies, Education and Science, its website, and the articles published therein are licensed under a 
