Forward Selection as a Feature Selection Method in the SVM Kernel for Student Graduation Data
DOI:
10.33395/sinkron.v8i4.14172Keywords:
Feature Selection, Forward Selection, Student Graduation, SVMAbstract
In the era of information technology development, accurate graduation predictions are important to improve the quality of higher education in Indonesia. This research evaluates the effectiveness of Support Vector Machine (SVM) with various kernels, including Radial Basis Function (RBF), linear, and polynomial, as well as the application of FS as an optimization method. The dataset used consists of student graduation data which includes nine independent attributes and one label. This research aims to increase the accuracy of student graduation predictions using the SVM method which is optimized through Forward Selection (FS). The SVM method is applied using 10-fold cross validation to predict on-time graduation. The results show that the combination of SVM and FS improves prediction accuracy significantly. The SVM model with an RBF kernel optimized with FS achieved the highest accuracy of 87.06% and recall of 53.68%, showing increased sensitivity in identifying student graduation cases compared to SVM without FS. Although there is a trade-off between precision and recall, the model optimized with FS shows better performance overall. This research contributes to the development of a more efficient graduation prediction method, which can help universities in planning strategies to improve academic quality. Further studies are recommended to overcome weaknesses in the recall value by using other optimization methods or combinations of other optimization algorithms
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Copyright (c) 2024 Hafis Nurdin, Irmawati Carolina, Resti Lia Andharsaputri, Anus Wuryanto, Ridwansyah
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