Enhancing Student Performance Prediction Using a Combined  SVM-Radial Basis Function Approach

Yuan Anisa; Winda Erika; Fadhillah Azmi

doi:10.55524/ijircst.2024.12.3.1

Abstract

This research aims to improve student performance predictions using a combined SVM (Support Vector Machine) and radial basis function (RBF) approach. The developed model utilizes a combination of the strengths of SVM in handling class separation and the ability of RBF to capture complex patterns in data. Student assessment data, including math, reading, and writing scores, is used as a feature to predict student performance on tests. Preprocessing steps, including feature normalization and label encoding, are applied to prepare the data for model training. Next, the SVM model with the RBF kernel is initialized and optimized using GridSearchCV to find the best parameters. Model evaluation was carried out using the R² metric to evaluate how well the model predicts student performance. Experimental results show that the combined SVM-RBF approach can improve student performance predictions with fairly accurate prediction results of 88%. The practical implication of this research is the development of a more accurate model for predicting student performance, which can be used as a tool to improve educational interventions and decision-making in educational institutions.

Keywords

Support Vector Machine, Radial Basis Function, Student Performance, Combined SVM, Student Assessment Data.

References

[1] K. Liu, J. Yao, D. Tao, and T. Yang, “Influence of Individual-technology-task-environment Fit on University Student Online Learning Performance: The Mediating Role of Behavioral, Emotional, and Cognitive Engagement,” Educ. Inf. Technol., vol. 28, no. 12, pp. 15949–15968, 2023, doi: 10.1007/s10639-023-11833-2.

[2] A. Dhankhar, K. Solanki, and A. Rathee, “International Journal of Advanced Trends in Computer Science and Engineering Available Online at http://www.warse.org/IJATCSE/static/pdf/file/ijatcse75842019.pdf Predicting Student ’ s Performance by using Classification Methods,” vol. 8, 2019.

[3] P. G. Student, “a Review of Student Performance Prediction Techniques in Virtual,” vol. 9, no. 8, pp. 183–190, 2021.

[4] F. Janan and S. K. Ghosh, “Prediction of student’s performance using support vector machine classifier,” Proc. Int. Conf. Ind. Eng. Oper. Manag., no. September, pp. 7078–7088, 2021, doi: 10.46254/an11.20211237.

[5] H. Wang, J. Xiong, Z. Yao, M. Lin, and J. Ren, “Research survey on support vector machine,” Int. Conf. Mob. Multimed. Commun., vol. 2017-July, pp. 95–103, 2017, doi: 10.475/eai.13-7-2017.2270596.

[6] S. Saikin, S. Fadli, and M. Ashari, “Optimization of Support Vector Machine Method Using Feature Selection to Improve Classification Results,” JISA(Jurnal Inform. dan Sains), vol. 4, no. 1, pp. 22–27, 2021, doi: 10.31326/jisa.v4i1.881.

[7] B. Gaye, D. Zhang, and A. Wulamu, “Improvement of Support Vector Machine Algorithm in Big Data Background,” Math. Probl. Eng., vol. 2021, 2021, doi: 10.1155/2021/5594899.

[8] H. Al Azies, D. Trishnanti, and E. Mustikawati P.H, “Comparison of Kernel Support Vector Machine (SVM) in Classification of Human Development Index (HDI),” IPTEK J. Proc. Ser., vol. 0, no. 6, p. 53, 2019, doi: 10.12962/j23546026.y2019i6.6339.

[9] M. K. Gibran and A. Saleh, “A Hybrid RBF Neural Network and FCM Clustering for Diabetes Prediction Dataset,” J. Comput. Sci. Inf. Technol. Telecommun. Eng., vol. 4, no. 2, pp. 395–401, 2023, doi: 10.30596/jcositte.v4i2.15573.

[10] A. Saleh, T. Tulus, and S. Efendi, “Analysis of Accurate Learning in Radial Basis Function Neural Network Using Cosine Similarity on Leaf Recognition,” 2019, doi: 10.4108/eai.20-1-2018.2281924.

[11] I. S. Al-Mejibli, J. K. Alwan, and D. H. Abd, “The effect of gamma value on support vector machine performance with different kernels,” Int. J. Electr. Comput. Eng., vol. 10, no. 5, pp. 5497–5506, 2020, doi: 10.11591/IJECE.V10I5.PP5497-5506.

[12] C. L. Ma and Y. B. Yuan, “A novel support vector machine with globality-locality preserving,” Sci. World J., vol. 2014, 2014, doi: 10.1155/2014/872697.

[13] A. V. Asimit, I. Kyriakou, S. Santoni, S. Scognamiglio, and R. Zhu, “Robust Classification via Support Vector Machines,” Risks, vol. 10, no. 8, pp. 1–25, 2022, doi: 10.3390/risks10080154.

[14] K. Saputra, “Perbandingan Kinerja Fungsi Kernel Algoritma Support Vector Machine Pada Klasifikasi Penyakit Padi,” Ijccs, vol. x, No.x, no. x, pp. 1–5, 2023.

[15] A. Z. Praghakusma and N. Charibaldi, “Komparasi Fungsi Kernel Metode Support Vector Machine untuk Analisis Sentimen Instagram dan Twitter (Studi Kasus?: Komisi Pemberantasan Korupsi),” JSTIE (Jurnal Sarj. Tek. Inform., vol. 9, no. 2, p. 88, 2021, doi: 10.12928/jstie.v9i2.20181.

[16] D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput. Sci., vol. 7, pp. 1–24, 2021, doi: 10.7717/PEERJ-CS.623.

Cites this article as

Y. Anisa, W. Erika, F. Azmi, "Enhancing Student Performance Prediction Using a Combined SVM-Radial Basis Function Approach", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), vol. 12, no. 3, pp. 1-5, 2024. doi: https://doi.org/10.55524/ijircst.2024.12.3.1

Corresponding Author

Yuan Anisa

Department of Electrical Engineering, Universitas Medan Area, Medan Indonesia

Download Full Paper

Download PDF

No. of Downloads: 82 | No. of Views: 4051

Enhancing Student Performance Prediction Using a Combined SVM-Radial Basis Function Approach

Citations

Total View 4051

Total Download 82