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Annisa Fathia Aziza
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Computer Science & IT

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Evaluasi dan Perbandingan Metode Klasifikasi Support Vector Machine dan K-Nearest Neighbor untuk Prediksi Kompetensi Lulusan Perguruan Tinggi Annisa Fathia Aziza; Hayati Noor; Rina Alfah
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 3 No. 4 (2025): JNovember :urnal Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jusiik-widyakarya.v3i4.6032

Abstract

The world of work is an environment related to the work we are currently in. In other words, it is a place where various individuals perform an activity. The quality of college graduates is not only seen in terms of high or good grades / GPA. There are many other considerations, where large companies see a potential possessed by the person concerned. The dataset in this study was taken from student respondents about the world of work. One way to classify the influence of competence on the world of work in machine learning is to use datasets as training data so that performance testing can be carried out with the right classification method. From the results of the tests carried out, it is concluded that the results of the comparison are different, which shows the accuracy value of KNN which is around 96%, while the results of the SVM accuracy tested are 98%, so that the accuracy of SVM is better than KNN.
Co-Authors Hayati Noor Rina Alfah