Comparison some of kernel functions with support vector machines classifier for thalassemia dataset

Wirasati, Ilsya; Rustam, Zuherman; Aurelia, Jane Eva; Hartini, Sri; Saragih, Glori Stephani

doi:10.11591/ijai.v10.i2.pp430-437

Akses terbuka Jurnal SINTA 1

Comparison some of kernel functions with support vector machines classifier for thalassemia dataset

IAES International Journal of Artificial Intelligence · Vol. 10 No. 2 (2021) · hlm. 430–437

DOI: 10.11591/ijai.v10.i2.pp430-437
ISSN: 2252-8938, 2089-4872
Terbit: 2021-06-01
Penerbit: Institute of Advanced Engineering and Science
Bahasa: eng
Lisensi: creativecommons.org/licenses/by-sa/4.0

Abstrak

In the medical field, accurate classification of medical data is really important because of its impact on disease detection and patient’s treatment. Technology, machine learning, is needed to help medical staff to improve accuracy to classify disease. This research discussed some kernel functions, such as gaussian radial basis function (RBF) kernel, Polynomial kernel, and linear kernel with support vector machine (SVM) to classify thalassemia data. Thalassemia is a genetic blood disorder which is also one of the major public health problems. In this paper, there is an explanation about thalassemia, SVM, and some of the kernel functions that serve as a comprehensive source for the next research about this topic. Furthermore, there is a comparison result from three kernel functions to find out which one has the best performance. The result is gaussian RBF kernel with SVM is the best method with an average of accuracy 99,63%.

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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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Tampilkan sebagai tabel

Rekan penulis	Organisasi	Artikel bersama
Ajeng Maharani Putri	University of Indonesia	3
Jacub Pandelaki	Rumah Sakit Umum Pusat Nasional Dr. Cipto Mangunkusumo	3
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

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

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 some of kernel functions with support vector machines classifier for thalassemia dataset</dc:title>
	<dc:creator>Wirasati, Ilsya</dc:creator>
	<dc:creator>Rustam, Zuherman</dc:creator>
	<dc:creator>Aurelia, Jane Eva</dc:creator>
	<dc:creator>Hartini, Sri</dc:creator>
	<dc:creator>Saragih, Glori Stephani</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Classification; Kernel function; Machine learning; Support vector machine; Thalasssemia</dc:subject>
	<dc:description xml:lang="en-US">In the medical field, accurate classification of medical data is really important because of its impact on disease detection and patient’s treatment. Technology, machine learning, is needed to help medical staff to improve accuracy to classify disease. This research discussed some kernel functions, such as gaussian radial basis function (RBF) kernel, Polynomial kernel, and linear kernel with support vector machine (SVM) to classify thalassemia data. Thalassemia is a genetic blood disorder which is also one of the major public health problems. In this paper, there is an explanation about thalassemia, SVM, and some of the kernel functions that serve as a comprehensive source for the next research about this topic. Furthermore, there is a comparison result from three kernel functions to find out which one has the best performance. The result is gaussian RBF kernel with SVM is the best method with an average of accuracy 99,63%. </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>2021-06-01</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/20849</dc:identifier>
	<dc:identifier>10.11591/ijai.v10.i2.pp430-437</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 10, No 2: June 2021; 430-437</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v10.i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/20849/13135</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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