Perbandingan Algoritma SVM dan KNN dalam Mengklasifikasi Kelulusan Mahasiswa pada Suatu Mata Kuliah

Shedriko Shedriko

doi:10.30998/string.v6i2.9160

Perbandingan Algoritma SVM dan KNN dalam Mengklasifikasi Kelulusan Mahasiswa pada Suatu Mata Kuliah

Shedriko Shedriko^(1*)

(1)
(*) Corresponding Author

Abstract

Two different algorithms will use different approaches. Like KNN (K-Nearest Neighbour) and SVM (Support Vector Machine), with the former calculates the closest distance between two instances, while the latter calculates the existence of hyperplane that separates two resulted classes. By using train data consisting of anomaly or noise data and using real measurement normally given by a lecturer to the students in the class to determine the students’ pass or fail in one subject, the research finds the difference between the two algorithms. The research is conducted in the University of XYZ on IIT (Introduction to Information Technology) subject. With the use of Orange Data Mining software, the research aims to give information about the suited algorithm for prediction or classification of student’s success in a related subject or others. It uses quantitative analysis with KNN dan SVM algorithms methods. Based on an assessment of several parameters, KNN is better than SVM, but SVM is better than KNN in obtaining a passing threshold value.

Keywords

SVM; KNN; success; classification; quantitative

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DOI: http://dx.doi.org/10.30998/string.v6i2.9160