Comparison of Kernel Function on Support Vector Machine in Classification of Childbirth

The maternal mortality rate during childbirth can be reduced through the efforts of the medical team in determining the childbirth process that must be undertaken immediately. Machine learning in terms of classifying childbirth can be a solution for the medical team in determining the childbirth pro...

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Autor principal:	Putroue Keumala Intan
Formato:	article
Lenguaje:	EN
Publicado:	Department of Mathematics, UIN Sunan Ampel Surabaya 2019
Materias:	svm childbirth kernel functions Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/1b047bd18fe94a8ab84358ce64837391
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spelling	oai:doaj.org-article:1b047bd18fe94a8ab84358ce648373912021-12-02T16:54:23ZComparison of Kernel Function on Support Vector Machine in Classification of Childbirth2527-31592527-316710.15642/mantik.2019.5.2.90-99https://doaj.org/article/1b047bd18fe94a8ab84358ce648373912019-10-01T00:00:00Zhttp://jurnalsaintek.uinsby.ac.id/index.php/mantik/article/view/683https://doaj.org/toc/2527-3159https://doaj.org/toc/2527-3167The maternal mortality rate during childbirth can be reduced through the efforts of the medical team in determining the childbirth process that must be undertaken immediately. Machine learning in terms of classifying childbirth can be a solution for the medical team in determining the childbirth process. One of the classification methods that can be used is the Support Vector Machine (SVM) method which is able to determine a hyperplane that will form a good decision boundary so that it is able to classify data appropriately. In SVM, there is a kernel function that is useful for solving non-linear classification cases by transforming data to a higher dimension. In this study, four kernel functions will be used; Linear, Radial Basis Function (RBF), Polynomial, and Sigmoid in the classification process of childbirth in order to determine the kernel function that is capable of producing the highest accuracy value. Based on research that has been done, it is obtained that the accuracy value generated by SVM with linear kernel functions is higher than the other kernel functions.Putroue Keumala IntanDepartment of Mathematics, UIN Sunan Ampel Surabayaarticlesvmchildbirthkernel functionsMathematicsQA1-939ENMantik: Jurnal Matematika, Vol 5, Iss 2, Pp 90-99 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	svm childbirth kernel functions Mathematics QA1-939
spellingShingle	svm childbirth kernel functions Mathematics QA1-939 Putroue Keumala Intan Comparison of Kernel Function on Support Vector Machine in Classification of Childbirth
description	The maternal mortality rate during childbirth can be reduced through the efforts of the medical team in determining the childbirth process that must be undertaken immediately. Machine learning in terms of classifying childbirth can be a solution for the medical team in determining the childbirth process. One of the classification methods that can be used is the Support Vector Machine (SVM) method which is able to determine a hyperplane that will form a good decision boundary so that it is able to classify data appropriately. In SVM, there is a kernel function that is useful for solving non-linear classification cases by transforming data to a higher dimension. In this study, four kernel functions will be used; Linear, Radial Basis Function (RBF), Polynomial, and Sigmoid in the classification process of childbirth in order to determine the kernel function that is capable of producing the highest accuracy value. Based on research that has been done, it is obtained that the accuracy value generated by SVM with linear kernel functions is higher than the other kernel functions.
format	article
author	Putroue Keumala Intan
author_facet	Putroue Keumala Intan
author_sort	Putroue Keumala Intan
title	Comparison of Kernel Function on Support Vector Machine in Classification of Childbirth
title_short	Comparison of Kernel Function on Support Vector Machine in Classification of Childbirth
title_full	Comparison of Kernel Function on Support Vector Machine in Classification of Childbirth
title_fullStr	Comparison of Kernel Function on Support Vector Machine in Classification of Childbirth
title_full_unstemmed	Comparison of Kernel Function on Support Vector Machine in Classification of Childbirth
title_sort	comparison of kernel function on support vector machine in classification of childbirth
publisher	Department of Mathematics, UIN Sunan Ampel Surabaya
publishDate	2019
url	https://doaj.org/article/1b047bd18fe94a8ab84358ce64837391
work_keys_str_mv	AT putrouekeumalaintan comparisonofkernelfunctiononsupportvectormachineinclassificationofchildbirth
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Comparison of Kernel Function on Support Vector Machine in Classification of Childbirth

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