Comparation between Linear and Polynomial Kernel Function for Ovarium Cancer Classification

R. S. Samosir, F. L. Gaol, B. S. Abbas, B. S. Sabarguna, Y. Heryadi

Research output: Contribution to journalConference article

Abstract

Ovarium cancer is a kind of cancer disease which often attacks a woman. Ovarium cancer consists of benign and malignant type. Based on the data set obtained, this research try to classify the data set with support vector machine algorithm. The principle of SVM is how to identify a hyperplanes with maximal margin. Hyperplanes is a border line between the data. Here, we will used two kernel function in SVM, linear and polynomial kernel function. Next We will compare both of them algorithm to reach the hyperplanes with maximal margin. The hyperplanes then used to predict the class of data testing given. Finally, comparation result will be obtained from confussion matrix and accuration calculation for each kernel function while used to classify the ovarium cancer data set. Accuration result with Linear kernel function is 68.98% for first schema and 66.67% for second schema. Then accuration result with Polynomial Kernel Function is 83.79% for both of schema.

Original languageEnglish
Article number012038
JournalJournal of Physics: Conference Series
Volume1235
Issue number1
DOIs
Publication statusPublished - 19 Jul 2019
Event3rd International Conference on Computing and Applied Informatics 2018, ICCAI 2018 - Medan, Sumatera Utara, Indonesia
Duration: 18 Sep 201819 Sep 2018

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