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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI
Indah Siti Rahmawati
Department of Statistics, Universitas Islam Bandung
Mathematics

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Classification of Unisba Students' Graduation Time using Support Vector Machine Optimized with Grid Search Algorithm Ilham Faishal Mahdy; Muthia Nadhira Faladiba; Nur Azizah Komara Rifai; Indah Siti Rahmawati; Andhika Sidiq Firmansyah
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 1 (2024): SEPTEMBER 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i1.36257

Abstract

Support Vector Machine is a classification method that finds the optimal hyperplane to separate two data classes. SVM has much better generalization performance than other methods. However, SVM needs to improve in determining hyperparameter values. Therefore, parameter optimization is necessary to determine the optimal hyperparameter value. Grid search is one of the parameter optimization methods that can improve the quality of SVM models. This study aims to assess the level of accuracy in predicting student graduation times by using five features that affect it. This study shows that the resulting SVM model optimized with the Grid Search Algorithm is quite consistent and prevents overfitting. By utilizing the results of SVM modelling, UNISBA is expected to improve the quality of graduates. The risk of delays in graduation can be considered early by paying attention to the background and achievements of students
Co-Authors Andhika Sidiq Firmansyah Ilham Faishal Mahdy Muthia Nadhira Faladiba Nur Azizah Komara Rifai