Comparison of kernel function on support vector machine in classification of childbirth

Intan, Putroue Keumala (2019) Comparison of kernel function on support vector machine in classification of childbirth. https://doi.org/10.15642/mantik.2019.5.2.90-99, 5 (2). pp. 90-99. ISSN 2527-3167; 2527-3159

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Abstract

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.

Item Type: Article
Additional Information: http://jurnalsaintek.uinsby.ac.id/index.php/mantik/article/view/683
Creators:
Creators
Email
NIDN
Intan, Putroue Keumala
puput.in@gmail.com
0728058802
Uncontrolled Keywords: SVM; childbirth; kernel functions
Subjects: 11 MEDICAL AND HEALTH SCIENCES > 1114 Paediatrics and Reproductive Medicine > 111401 Foetal Development and Medicine
13 EDUCATION > 1302 Curriculum and Pedagogy > 130209 Medicine, Nursing and Health Curriculum and Pedagogy
Divisions: Fakultas Sains dan Teknologi > Prodi Matematika
Depositing User: Samidah Nurmayuni
Date Deposited: 16 Jun 2022 04:24
Last Modified: 16 Jun 2022 04:24
URI: http://repository.uinsby.ac.id/id/eprint/2549

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