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
Putroue Keumala Intan_Comparison of kernel function on support vector machine in classification of childbirth.pdf
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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 |
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Additional Information: | http://jurnalsaintek.uinsby.ac.id/index.php/mantik/article/view/683 |
Creators: | Creators Email ["eprint_fieldname_creators_NIDN" not defined] 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.uinsa.ac.id/id/eprint/2549 |