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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

40 authors · 107 co-authorship links · 12 across different organisations · showing the 40 most connected

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

  • Universitas Halu Oleo
  • Hasanuddin University
  • Universitas Sembilanbelas November Kolaka
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Sutardi SutardiUniversitas Halu Oleo4
Bambang PramonoUniversitas Halu Oleo3
Nurmaladewi NurmaladewiUniversitas Halu Oleo3
Isnawaty IsnawatyUniversitas Halu Oleo2
LM TajidunUniversitas Halu Oleo2
La SurimiUniversitas Halu Oleo2
Ilham Julian EfendiUniversitas Halu Oleo1
Muhammad Gibran Fitrah HidayahtullahUniversitas Halu Oleo1
Isnawaty IsnawatyAffiliation unknown1
Akhmad IrsyadAffiliation unknown1
Muh Iskandar Z. AAffiliation unknown1
Muh. Ihsan SaritaUniversitas Halu Oleo1
Djafar DjafarAffiliation unknown1
Jumadil NangiUniversitas Halu Oleo1
Ilayani IlayaniAffiliation unknown1
Statiswaty StatiswatyUniversitas Halu Oleo1
Rizaldy Setiawan HasanuddinUniversitas Halu Oleo1
Sulastri ApridayantiUniversitas Halu Oleo1
Andi Maghfirah ParenrengiAffiliation unknown1
Ika Purwanti NingrumUniversitas Halu Oleo1
Roy PaneAffiliation unknown1
Sumarni SumarniHasanuddin University1
Etrilian TongaluUniversitas Halu Oleo1
Hayara OctavianiUniversitas Halu Oleo1
Sitti Nur AisyahHasanuddin University1
Nur SamsuriatiAffiliation unknown1
Fifi Nirmala GUniversitas Halu Oleo1
Rizki Eka Sakti OctavianiAffiliation unknown1
L.M Bahtiar AksaraAffiliation unknown1
Wd Rizky AwaliahAffiliation unknown1
Erwin AnshariAffiliation unknown1
Adha Mashur SajiahUniversitas Halu Oleo1
Riski Eka Sakti OctavianiUniversitas Halu Oleo1
Nur Fajriah MuchlisUniversitas Halu Oleo1
Jelyzer PatananAffiliation unknown1
Muhammad Abdillah RahmatHasanuddin University1
Ade Sitti Nur ZainabAffiliation unknown1

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 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<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>