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Jurnal SINTA 2

AUTOMATIC ARRHYTHMIAS DETECTION USING VARIOUS TYPES OF ARTIFICIAL NEURAL NETWORK BASED LEARNING VECTOR QUANTIZATION (LVQ)

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 7 No. 2 (2014) · pp. 90–100

DOI
10.21609/jiki.v7i2.262
ISSN
2502-9274, 2088-7051
Published
2014-08-21
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Abstract An automatic Arrythmias detection system is urgently required due to small number of cardiologits in Indonesia. This paper discusses only about the study and implementation of the system. We use several kinds of signal processing methods to recognize arrythmias from ecg signal. The core of the system is classification. Our LVQ based artificial neural network classifiers based on LVQ, which includes LVQ1, LVQ2, LVQ2.1, FNLVQ, FNLVQ MSA, FNLVQ-PSO, GLVQ and FNGLVQ. Experiment result show that for non round robin dataset, the system could reach accuracy of 94.07%, 92.54%, 88.09%, 86.55%, 83.66%, 82.29 %, 82.25%, and 74.62% respectively for FNGLVQ, FNLVQ-PSO, GLVQ, LVQ2.1, FNLVQ-MSA, LVQ2, FNLVQ and LVQ1. Whereas for round robin dataset, system reached accuracy of 98.12%, 98.04%, 94.31%, 90.43%, 86.75%, 86.12 %, 84.50%, and 74.78% respectively for GLVQ, LVQ2.1, FNGLVQ, FNLVQ-PSO, LVQ2, FNLVQ-MSA, FNLVQ and LVQ1.

Keywords

Collaboration network of the authors

13 authors · 25 co-authorship links · 18 across different organisations

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

  • University of Indonesia
  • Institut Teknologi Sepuluh Nopember
  • Universitas Negeri Surabaya
  • Binus University
  • University of Edinburgh
  • Diponegoro University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Mohammad Isa IrawanInstitut Teknologi Sepuluh Nopember2
Muhammad Febrian RachmadiUniversity of Edinburgh2
Tjan BasaruddinUniversity of Indonesia1
Adi WibowoDiponegoro University1
Muhammad Syaeful FajarUniversity of Indonesia1
Fadhlurrahman AfifUniversity of Indonesia1
Muhammad AthoillahInstitut Teknologi Sepuluh Nopember1
Umi MahdiyahInstitut Teknologi Sepuluh Nopember1
Zumrotun NafisahUniversitas Negeri Surabaya1

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">AUTOMATIC ARRHYTHMIAS DETECTION USING VARIOUS TYPES OF ARTIFICIAL NEURAL NETWORK BASED LEARNING VECTOR QUANTIZATION (LVQ)</dc:title>
	<dc:creator>Fitria, Diane</dc:creator>
	<dc:creator>Ma'sum, Muhammad Anwar</dc:creator>
	<dc:creator>Imah, Elly Matul</dc:creator>
	<dc:creator>Gunawan, Alexander Agung</dc:creator>
	<dc:subject xml:lang="en-US">Automatic Arrythmias detection</dc:subject>
	<dc:subject xml:lang="en-US">ECG</dc:subject>
	<dc:subject xml:lang="en-US">Classification</dc:subject>
	<dc:subject xml:lang="en-US">LVQ1</dc:subject>
	<dc:subject xml:lang="en-US">LVQ2</dc:subject>
	<dc:subject xml:lang="en-US">LVQ2.1</dc:subject>
	<dc:subject xml:lang="en-US">FNLVQ</dc:subject>
	<dc:subject xml:lang="en-US">FNLVQ MSA</dc:subject>
	<dc:subject xml:lang="en-US">FNLVQ-PSO</dc:subject>
	<dc:subject xml:lang="en-US">GLVQ</dc:subject>
	<dc:subject xml:lang="en-US">FNGLVQ</dc:subject>
	<dc:description xml:lang="en-US">Abstract 

An automatic Arrythmias detection system is urgently required due to small number of cardiologits in Indonesia. This paper discusses only about the study and implementation of the system. We use several kinds of signal processing methods to recognize arrythmias from ecg signal. The core of the system is classification. Our LVQ based artificial neural network classifiers based on LVQ, which includes LVQ1, LVQ2, LVQ2.1, FNLVQ, FNLVQ MSA, FNLVQ-PSO, GLVQ and FNGLVQ. Experiment result show that for non round robin dataset, the system could reach  accuracy of 94.07%, 92.54%,  88.09% , 86.55% , 83.66%, 82.29 %, 82.25%, and 74.62% respectively for FNGLVQ, FNLVQ-PSO, GLVQ, LVQ2.1, FNLVQ-MSA, LVQ2, FNLVQ and LVQ1. Whereas for round robin dataset, system reached accuracy of 98.12%, 98.04%, 94.31%, 90.43%, 86.75%, 86.12 %, 84.50%, and 74.78%   respectively for GLVQ, LVQ2.1, FNGLVQ, FNLVQ-PSO, LVQ2, FNLVQ-MSA, FNLVQ and LVQ1.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2014-08-21</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/262</dc:identifier>
	<dc:identifier>10.21609/jiki.v7i2.262</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 7 No. 2 (2014): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 90-100</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v7i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/262/320</dc:relation>
</oai_dc:dc>