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Arif Pratama
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Implementasi Algoritme Support Vector Machine (SVM) untuk Prediksi Ketepatan Waktu Kelulusan Mahasiswa Arif Pratama; Randy Cahya Wihandika; Dian Eka Ratnawati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 4 (2018): April 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Graduate on time is the desire of all students. In reality, not as expected many students who graduated more than four years. necessitating the application of predictive graduation students can classify graduation prediction data based on parameters that have been determined. Because it is necessary for the application of intelligent systems can classify graduation prediction data based on parameters. Algorithm Support Vector Machine (SVM) to classify the data into two classes using kernel Gaussian RBF with a combined value of parameter λ = 0,5, constant γ = 0,01, and ε (epsilon) = 0,001 itermax = 100, c = 1 by using training data as much as 170 datasets , this study resulted in an average accuracy of 80,55 %.
Co-Authors Dian Eka Ratnawati Randy Cahya Wihandika