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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
10.15294/0xga4r13
ISSN
2986-6588, 2963-5551
Terbit
2024-03-31
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

Abstract. 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. Purpose:To find out the accuracy results obtained in the fine-tuning optimization model on Convolutional Neural Network (CNN) method in classifying images in eye disease. Methods/Study design/approach: Combining the Convolutional Neural Network (CNN) method with fine-tuning optimization models for image classification in eye disease. The two methods will be compared to determine the best result. Result/Findings: The accuracy results obtained from testing the Convolutional Neural Network method with the VGG16 architecture were 82.63% while the accuracy results from testing the fine-tuning model were 94.13%. Novelty/Originality/Value: The test results on the fine-tuning model have better accuracy than the testing of the Convolutional Neural Network method. This can be seen in the fine-tuning model which has an increase in accuracy of 11.5%.

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  • State University of Semarang
  • Universitas Nahdlatul Ulama Blitar
  • Institut Teknologi Kalimantan
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Rekan penulisOrganisasiArtikel bersama
Muh. SholehAfiliasi tidak diketahui2
Martanto Setyo HusodoAfiliasi tidak diketahui2
Riza ArifudinState University of Semarang2
Deonisius Germandy Cahaya PutraAfiliasi tidak diketahui2
Resta AdityatamaAfiliasi tidak diketahui2
Taripar Matius AlexanderAfiliasi tidak diketahui1
Florentina Yuni AriniState University of Semarang1
Athoillah AthoillahAfiliasi tidak diketahui1
Endang SugihartiState University of Semarang1
Kholiq BudimanAfiliasi tidak diketahui1
zaenal abidinAfiliasi tidak diketahui1
Alfath YanuartoAfiliasi tidak diketahui1
Dinar Diaz SeptianAfiliasi tidak diketahui1
Akhmad MunawarAfiliasi tidak diketahui1
Andika Enggal RamadhanAfiliasi tidak diketahui1
Bhekti KumorowatiAfiliasi tidak diketahui1
Sugianto SugiantoUniversitas Nahdlatul Ulama Blitar1
Cahya WulandariState University of Semarang1
Muhamad Sayuti HassanAfiliasi tidak diketahui1
Zidney Ilma Fazaada EmhaAfiliasi tidak diketahui1
Angel Novita SinuratAfiliasi tidak diketahui1
Tazkiyatun Aeni AmaliyahAfiliasi tidak diketahui1
Devi Tri WidyantiAfiliasi tidak diketahui1
Much Azis MuslimAfiliasi tidak diketahui1
Ahmad WarsunAfiliasi tidak diketahui1
Pradistia Edo KristiantoAfiliasi tidak diketahui1
Syehudin SyehudinAfiliasi tidak diketahui1
Alamsyah AlamsyahInstitut Teknologi Kalimantan1
Hendi SusantoAfiliasi tidak diketahui1
Annisya Dira PrastiwiAfiliasi tidak diketahui1
Adham Satria FirmansyahAfiliasi tidak diketahui1
Luthfi Ilham Agus PratamaAfiliasi tidak diketahui1
Muhammad Zaenal MuttaqinAfiliasi tidak diketahui1
Dian Tri WiyantiState University of Semarang1
Dante AlighiriState University of Semarang1
Priyantini WidiyaningrumAfiliasi tidak diketahui1
Agus YuliantoAfiliasi tidak diketahui1

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/journals/rji/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (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">Optimization of the Convolutional Neural Network Method Using Fine-Tuning for Image Classification of Eye Disease</dc:title>
	<dc:creator xml:lang="en">Wulandari, Vivi</dc:creator>
	<dc:creator xml:lang="en">Putra, Anggyi Trisnawan</dc:creator>
	<dc:subject xml:lang="en">Eye Disease</dc:subject>
	<dc:subject xml:lang="en">Convolutional Neural Network</dc:subject>
	<dc:subject xml:lang="en">Fine-Tuning</dc:subject>
	<dc:subject xml:lang="en">VGG16</dc:subject>
	<dc:subject xml:lang="en">Deep Learning</dc:subject>
	<dc:description xml:lang="en">Abstract. 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.
Purpose:To find out the accuracy results obtained in the fine-tuning optimization model on Convolutional Neural Network (CNN) method in classifying images in eye disease.
Methods/Study design/approach: Combining the Convolutional Neural Network (CNN) method with fine-tuning optimization models for image classification in eye disease. The two methods will be compared to determine the best result.
Result/Findings: The accuracy results obtained from testing the Convolutional Neural Network method with the VGG16 architecture were 82.63% while the accuracy results from testing the fine-tuning model were 94.13%.
Novelty/Originality/Value: The test results on the fine-tuning model have better accuracy than the testing of the Convolutional Neural Network method. This can be seen in the fine-tuning model which has an increase in accuracy of 11.5%.</dc:description>
	<dc:publisher xml:lang="en">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:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/rji/article/view/34944</dc:identifier>
	<dc:identifier>10.15294/0xga4r13</dc:identifier>
	<dc:source xml:lang="en">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/krhn0z16</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/rji/article/view/34944/6340</dc:relation>
</oai_dc:dc>