Comparison between fuzzy kernel k-medoids using radial basis function kernel and polynomial kernel function in hepatitis classification

Stephani Saragih, Glori; Hartini, Sri; Rustam, Zuherman

doi:10.11591/ijai.v10.i1.pp60-65

Akses terbuka Jurnal SINTA 1

Comparison between fuzzy kernel k-medoids using radial basis function kernel and polynomial kernel function in hepatitis classification

IAES International Journal of Artificial Intelligence · Vol. 10 No. 1 (2021) · hlm. 60–65

DOI: 10.11591/ijai.v10.i1.pp60-65
ISSN: 2252-8938, 2089-4872
Terbit: 2021-03-01
Penerbit: Institute of Advanced Engineering and Science
Bahasa: eng
Lisensi: creativecommons.org/licenses/by-sa/4.0

Abstrak

This paper compares the fuzzy kernel k-medoids using radial basis function (RBF) and polynomial kernel function in hepatitis classification. These two kernel functions were chosen due to their popularity in any kernel-based machine learning method for solving the classification task. The hepatitis dataset then used to evaluate the performance of both methods that were expected to provide an accurate diagnosis in patients to obtain treatment at an early phase. The data were obtained from two hospitals in Indonesia, consisting of 89 hepatitis-B and 31 hepatitis-C samples. The data were analyzed using several cases of k-fold cross-validation, and the performances were compared according to their accuracy, sensitivity, precision, F1-Score, and running time. From the experiments, it was concluded that fuzzy kernel k-medoids using RBF kernel function is better compared to polynomial kernel function with the 6% increment of accuracy, 13% enhancement of sensitivity, and 5% improvement in F1-Score. On the other side, the precision of fuzzy kernel k-medoids using polynomial kernel function is 2% higher than using the RBF kernel function. According to the results, the use of RBF or polynomial kernel function in fuzzy kernel medoids can be considered according to the primary goal of the classification.

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11 penulis · 29 tautan kepenulisan bersama · 7 lintas organisasi

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University of Indonesia
Rumah Sakit Umum Pusat Nasional Dr. Cipto Mangunkusumo
Universidad Complutense de Madrid

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Rekan penulis	Organisasi	Artikel bersama
Ilsya Wirasati	University of Indonesia	8
Jane Eva Aurelia	University of Indonesia	6
Yasirly Amalia	University of Indonesia	3
Aldi Purwanto	University of Indonesia	3
María Jesús Segovia Vargas	Universidad Complutense de Madrid	3
Fildzah Zhafarina	University of Indonesia	3
Ajeng Maharani Putri	University of Indonesia	2
Jacub Pandelaki	Rumah Sakit Umum Pusat Nasional Dr. Cipto Mangunkusumo	2

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.

Metadata asli (oai_dc)

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	<dc:title xml:lang="en-US">Comparison between fuzzy kernel k-medoids using radial basis function kernel and polynomial kernel function in hepatitis classification</dc:title>
	<dc:creator>Stephani Saragih, Glori</dc:creator>
	<dc:creator>Hartini, Sri</dc:creator>
	<dc:creator>Rustam, Zuherman</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Classification; Fuzzy kernel k-medoids; Hepatitis; Kernel function; Polynomial kernel; Radial basis function</dc:subject>
	<dc:description xml:lang="en-US">This paper compares the fuzzy kernel k-medoids using radial basis function (RBF) and polynomial kernel function in hepatitis classification. These two kernel functions were chosen due to their popularity in any kernel-based machine learning method for solving the classification task. The hepatitis dataset then used to evaluate the performance of both methods that were expected to provide an accurate diagnosis in patients to obtain treatment at an early phase. The data were obtained from two hospitals in Indonesia, consisting of 89 hepatitis-B and 31 hepatitis-C samples. The data were analyzed using several cases of k-fold cross-validation, and the performances were compared according to their accuracy, sensitivity, precision, F1-Score, and running time. From the experiments, it was concluded that fuzzy kernel k-medoids using RBF kernel function is better compared to polynomial kernel function with the 6% increment of accuracy, 13% enhancement of sensitivity, and 5% improvement in F1-Score. On the other side, the precision of fuzzy kernel k-medoids using polynomial kernel function is 2% higher than using the RBF kernel function. According to the results, the use of RBF or polynomial kernel function in fuzzy kernel medoids can be considered according to the primary goal of the classification.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2021-03-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/20811</dc:identifier>
	<dc:identifier>10.11591/ijai.v10.i1.pp60-65</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 10, No 1: March 2021; 60-65</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v10.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/20811/13054</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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