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Optimization of the Convolutional Neural Network Method Using Fine-Tuning for Image Classification of Eye Disease

Recursive Journal of Informatics · Vol. 2 No. 1 (2024) · hlm. 54–61

DOI
10.15294/rji.v2i1.73625
ISSN
2986-6588, 2963-5551
Terbit
2024-03-31
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

The eye is the most important organ of the human body which functions as the sense of sight. Most people wish they had healthy eyes so they could see clearly about life around them. However, some people experience eye health problems. There are many types of eye diseases ranging from mild to severe. With advances in technology, artificial intelligence can be used to classify eye diseases accurately, one of which is deep learning. Therefore, this study uses the Convolutional Neural Network (CNN) algorithm to classify eye diseases using the VGG16 architecture as a base model and will be combined using a fine-tuning model as an optimization to improve accuracy.

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23 penulis · 32 tautan kepenulisan bersama · 4 lintas organisasi

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  • State University of Semarang
  • National University of Malaysia
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Rekan penulisOrganisasiArtikel bersama
Deonisius Germandy Cahaya PutraState University of Semarang2
Resta AdityatamaState University of Semarang2
Muh. SholehState University of Semarang1
Martanto Setyo HusodoState University of Semarang1
Taripar Matius AlexanderState University of Semarang1
Florentina Yuni AriniState University of Semarang1
Riza ArifudinState University of Semarang1
Athoillah AthoillahState University of Semarang1
Cahya WulandariState University of Semarang1
Muhamad Sayuti HassanNational University of Malaysia1
Zidney Ilma Fazaada EmhaState University of Semarang1
Sugianto SugiantoState University of Semarang1
Angel Novita SinuratState University of Semarang1
Tazkiyatun Aeni AmaliyahState University of Semarang1
Ahmad WarsunState University of Semarang1
Pradistia Edo KristiantoState University of Semarang1
Syehudin SyehudinState University of Semarang1
Annisya Dira PrastiwiState University of Semarang1
Adham Satria FirmansyahState University of Semarang1
Luthfi Ilham Agus PratamaState University of Semarang1
Muhammad Zaenal MuttaqinState University of Semarang1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/sju/index.php/rji/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 6 Nov 2026.
Metadata asli (oai_dc)
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	<dc:title xml:lang="en-US">Optimization of the Convolutional Neural Network Method Using Fine-Tuning for Image Classification of Eye Disease</dc:title>
	<dc:creator>Wulandari, Vivi</dc:creator>
	<dc:creator>Putra, Anggyi Trisnawan</dc:creator>
	<dc:description xml:lang="en-US">The eye is the most important organ of the human body which functions as the sense of sight. Most people wish they had healthy eyes so they could see clearly about life around them. However, some people experience eye health problems. There are many types of eye diseases ranging from mild to severe. With advances in technology, artificial intelligence can be used to classify eye diseases accurately, one of which is deep learning. Therefore, this study uses the Convolutional Neural Network (CNN) algorithm to classify eye diseases using the VGG16 architecture as a base model and will be combined using a fine-tuning model as an optimization to improve accuracy.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2024-03-31</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://journal.unnes.ac.id/sju/rji/article/view/73625</dc:identifier>
	<dc:identifier>10.15294/rji.v2i1.73625</dc:identifier>
	<dc:source xml:lang="en-US">Recursive Journal of Informatics; Vol 2 No 1 (2024): March 2024; 54-61</dc:source>
	<dc:source>2986-6588</dc:source>
	<dc:source>2963-5551</dc:source>
	<dc:source>10.15294/rji.v2i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/sju/rji/article/view/73625/26556</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Recursive Journal of Informatics</dc:rights>
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