Melola Discovery

← Melola Discovery

Jurnal SINTA 2

MACULAR EDEMA CLASSIFICATION USING SELF-ORGANIZING MAP AND GENERALIZED LEARNING VECTOR QUANTIZATION

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 7 No. 2 (2014) · pp. 54–60

DOI
10.21609/jiki.v7i2.257
ISSN
2502-9274, 2088-7051
Published
2014-08-21
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Abstract Macular edema is a kind of human sight disease as a result of advanced stage of diabetic retinopathy. It affects the central vision of patients and in severe cases lead to blindness. However, it is still difficult to diagnose the grade of macular edema quickly and accurately even by the medical doctor's skill. This paper proposes a new method to classify fundus images of diabetics by combining Self-Organizing Maps (SOM) and Generalized Vector Quantization (GLVQ) that will produce optimal weight in grading macular edema disease class. The proposed method consists of two learning phases. In the first phase, SOM is used to obtain the optimal weight based on dataset and random weight input. The second phase, GLVQ is used as main method to train data based on optimal weight gained from SOM. Final weights from GLVQ are used in fundus image classification. Experimental result shows that the proposed method is good for classification, with accuracy, sensitivity, and specificity at 80%, 100%, and 60%, respectively.

Keywords

Collaboration network of the authors

5 authors · 6 co-authorship links · 2 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
  • Universitas Halu Oleo
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ilham Julian EfendiUniversitas Halu Oleo1
Muhammad Gibran Fitrah HidayahtullahUniversitas Halu Oleo1

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) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">MACULAR EDEMA CLASSIFICATION USING SELF-ORGANIZING MAP AND GENERALIZED LEARNING VECTOR QUANTIZATION</dc:title>
	<dc:creator>Saputra, Rizal Adi</dc:creator>
	<dc:creator>Pasrun, Yuwanda Purnamasari</dc:creator>
	<dc:creator>Basyarah, Amaliya Nurani</dc:creator>
	<dc:subject xml:lang="en-US">Fundus Image</dc:subject>
	<dc:subject xml:lang="en-US">GLVQ</dc:subject>
	<dc:subject xml:lang="en-US">Macular Edema</dc:subject>
	<dc:subject xml:lang="en-US">SOM</dc:subject>
	<dc:description xml:lang="en-US">Abstract

Macular edema is a kind of human sight disease as a result of advanced stage of diabetic retinopathy.  It affects the central vision of patients and in severe cases lead to blindness. However, it is still difficult to diagnose the grade of macular edema quickly and accurately even by the medical doctor's skill. This paper proposes a new method to classify fundus images of diabetics by combining Self-Organizing Maps (SOM) and Generalized Vector Quantization (GLVQ) that will produce optimal weight in grading macular edema disease class. The proposed method consists of two learning phases. In the first phase, SOM is used to obtain the optimal weight based on dataset and random weight input. The second phase, GLVQ is used as main method to train data based on optimal weight gained from SOM. Final weights from GLVQ are used in fundus image classification. Experimental result shows that the proposed method is good for classification, with accuracy, sensitivity, and specificity at 80%, 100%, and 60%, respectively.</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/257</dc:identifier>
	<dc:identifier>10.21609/jiki.v7i2.257</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); 54-60</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/257/315</dc:relation>
</oai_dc:dc>