Acute sinusitis data classification using grey wolf optimization-based support vector machine

Putri, Ajeng Maharani; Rustam, Zuherman; Pandelaki, Jacub; Wirasati, Ilsya; Hartini, Sri

doi:10.11591/ijai.v10.i2.pp438-445

Akses terbuka Jurnal SINTA 1

Acute sinusitis data classification using grey wolf optimization-based support vector machine

IAES International Journal of Artificial Intelligence · Vol. 10 No. 2 (2021) · hlm. 438–445

DOI: 10.11591/ijai.v10.i2.pp438-445
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

Acute sinusitis is the most common form of sinusitis, and it causes swelling and inflammation within the nose. The main thing that can causes sinusitis is probably due to viruses, and also can be caused by other factors, namely bacteria, fungi, irritation, dust, and allergens. In this research, the CT scan data attributes will be used for classification and grey wolf optimization-support vector machine (GWO-SVM) will be the machine learning technique used, where the GWO technique will be used to tuned the parameters in SVM. The performance of methods was analyzed using the python programming language with different percentages of training data, which started from 10% to 90%. The GWO-SVM method proposed provides better accuracy than using SVM without GWO.

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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
Glori Stephani Saragih	University of Indonesia	16
Jane Eva Aurelia	University of Indonesia	6
Yasirly Amalia	University of Indonesia	2
Aldi Purwanto	University of Indonesia	2
María Jesús Segovia Vargas	Universidad Complutense de Madrid	2
Fildzah Zhafarina	University of Indonesia	2

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">Acute sinusitis data classification using grey wolf optimization-based support vector machine</dc:title>
	<dc:creator>Putri, Ajeng Maharani</dc:creator>
	<dc:creator>Rustam, Zuherman</dc:creator>
	<dc:creator>Pandelaki, Jacub</dc:creator>
	<dc:creator>Wirasati, Ilsya</dc:creator>
	<dc:creator>Hartini, Sri</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Acute sinusitis; Grey wolf optimization; Meta-heuristic; Support vector machine</dc:subject>
	<dc:description xml:lang="en-US">Acute sinusitis is the most common form of sinusitis, and it causes swelling and inflammation within the nose. The main thing that can causes sinusitis is probably due to viruses, and also can be caused by other factors, namely bacteria, fungi, irritation, dust, and allergens. In this research, the CT scan data attributes will be used for classification and grey wolf optimization-support vector machine (GWO-SVM) will be the machine learning technique used, where the GWO technique will be used to tuned the parameters in SVM. The performance of methods was analyzed using the python programming language with different percentages of training data, which started from 10% to 90%. The GWO-SVM method proposed provides better accuracy than using SVM without GWO.</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>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/20851</dc:identifier>
	<dc:identifier>10.11591/ijai.v10.i2.pp438-445</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 10, No 2: June 2021; 438-445</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/20851/13227</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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