STUDY COMPARISON BACKPROPOGATION, SUPPORT VECTOR MACHINE, AND EXTREME LEARNING MACHINE FOR BIOINFORMATICS DATA

mahdiyah, umi; Irawan, M. Isa; Imah, Elly Matul

doi:10.21609/jiki.v8i1.284

Jurnal SINTA 2

STUDY COMPARISON BACKPROPOGATION, SUPPORT VECTOR MACHINE, AND EXTREME LEARNING MACHINE FOR BIOINFORMATICS DATA

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 8 No. 1 (2015) · pp. 53–59

DOI: 10.21609/jiki.v8i1.284
ISSN: 2502-9274, 2088-7051
Published: 2015-03-26
Publisher: Faculty of Computer Science - Universitas Indonesia
Language: eng

Abstract

A successful understanding on how to make computers learn would open up many new uses of computers and new levels of competence and customization. A detailed understanding on information- processing algorithms for machine learning might lead to a better understanding of human learning abilities and disabilities. There are many type of machine learning that we know, which includes Backpropagation (BP), Extreme Learning Machine (ELM), and Support Vector Machine (SVM). This research uses five data that have several characteristics. The result of this research is all the three investigated models offer comparable classification accuracies. This research has three type conclusions, the best performance in accuracy is BP, the best performance in stability is SVM and the best performance in CPU time is ELM for bioinformatics data.

Keywords

Collaboration network of the authors

10 authors · 16 co-authorship links · 12 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

Institut Teknologi Sepuluh Nopember
University of Indonesia
Universitas Negeri Surabaya
Binus University
University of Edinburgh

Different organisations
Same organisation
Affiliation unknown
Thicker line = more joint articles · bigger circle = more articles

Show as table

Co-author	Organisation	Joint articles
Muhammad Anwar Ma’Sum	University of Indonesia	2
Alexander Agung Santoso Gunawan	Binus University	2
Muhammad Athoillah	Institut Teknologi Sepuluh Nopember	2
Tjan Basaruddin	University of Indonesia	1
Diane Fitria	University of Indonesia	1
Muhammad Febrian Rachmadi	University of Edinburgh	1
Zumrotun Nafisah	Universitas Negeri Surabaya	1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://jiki.cs.ui.ac.id/index.php/jiki/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en-US">STUDY COMPARISON BACKPROPOGATION, SUPPORT VECTOR MACHINE, AND EXTREME LEARNING MACHINE FOR BIOINFORMATICS DATA</dc:title>
	<dc:creator>mahdiyah, umi</dc:creator>
	<dc:creator>Irawan, M. Isa</dc:creator>
	<dc:creator>Imah, Elly Matul</dc:creator>
	<dc:subject xml:lang="en-US">Machine Learning</dc:subject>
	<dc:subject xml:lang="en-US">Backpropagation</dc:subject>
	<dc:subject xml:lang="en-US">Extreme Learning Machine</dc:subject>
	<dc:subject xml:lang="en-US">Support Vector Machine</dc:subject>
	<dc:subject xml:lang="en-US">Bioinformatics</dc:subject>
	<dc:description xml:lang="en-US">A successful understanding on how to make computers learn would open up many new uses of computers and new levels of competence and customization. A detailed understanding on information- processing algorithms for machine learning might lead to a better understanding of human learning abilities and disabilities. There are many type of machine learning that we know, which includes Backpropagation (BP), Extreme Learning Machine (ELM), and Support Vector Machine (SVM). This research uses five data that have several characteristics. The result of this research is all the three investigated models offer comparable classification accuracies. This research has three type conclusions, the best performance in accuracy is BP, the best performance in stability is SVM and the best performance in CPU time is ELM for bioinformatics data.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2015-03-26</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 8 No. 1 (2015): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 53-59</dc:source>
	<dc:source>2502-9274</dc:source>
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