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All Journal Jurnal Ilmu Komputer dan Informasi Jupiter Jurnal Buana Informatika Jurnal Informatika dan Teknik Elektro Terapan Register: Jurnal Ilmiah Teknologi Sistem Informasi SemanTIK : Teknik Informasi Jurnal Teknologi Sistem Informasi dan Aplikasi Community Development Journal: Jurnal Pengabdian Masyarakat Community Empowerment Jurnal Sains Teknologi dan Sistem Informasi
Yuwanda Purnamasari Pasrun
Universitas Sembilanbelas November Kolaka
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Public Health Social Sciences

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Journal : Jurnal Ilmu Komputer dan Informasi

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MACULAR EDEMA CLASSIFICATION USING SELF-ORGANIZING MAP AND GENERALIZED LEARNING VECTOR QUANTIZATION Rizal Adi Saputra; Yuwanda Purnamasari Pasrun; Amaliya Nurani Basyarah
Jurnal Ilmu Komputer dan Informasi Vol 7, No 2 (2014): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (294.548 KB) | DOI: 10.21609/jiki.v7i2.257

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.
Co-Authors Aliniy Aliniy Amaliya Nurani Basyarah Andi Tenri Sumpala Chastine Fatichah Djafar Djafar Fathur Rahman Rustan Haryati Haryati Ilayani, Ilayani Indar Ismail Jamaluddin Jayanti Yusmah Sari Jumadil Nangi, Jumadil La Ode Hasnuddin S. Sagala La Sudarman Mardiawati Mardiawati Muh Iskandar Z. A Muh. Ihsan Sarita Muhammad Syaiful Muliyadi Muliyadi Mutmainnah Muchtar Nanik Suciati Nasarudin Nasarudin Natalis Ransi Nisa Miftachurohmah Noorhasanah Z Nur Fajriah Muchlis Nurfitria Ningsi Phradiansah Phradiansah Rahmat Karim Rasmiati Rasyid Rizal Adi Saputra Sarimuddin, Sarimuddin Suharsono Bantun Sutardi Sutardi