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Milkfish Freshness Classification Using Convolutional Neural Networks Based on Resnet50 Architecture
Advance Sustainable Science, Engineering and Technology (ASSET)
ISSN 2715-4211, 2715-4211
Terbit 2023-10-31
Penerbit Universitas PGRI Semarang
Bahasa eng
Abstrak
Milkfish (Chanos chanos) had become the main commodity in three major cities in Indonesia, contributed at least 77 thousand tons of aquaculture production in 2021. The quality of fish is determined based on the level of freshness carried out in the sorting process, the sorting process is generally done by evaluating physical characteristics of the fish. However, this method is still considered less efficient and economical because the ability to classify the freshness level of fish can vary for each individual. In this study, by utilizing deep learning, a classification method for milkfish freshness level classification with ResNet50 architecture is proposed, the proposed method is purposed to overcome the previously stated problems, thus creating an efficient and economical system. By creating an efficient system, milkfish sorting process can be carried out quicker and more accurately. Using personal dataset divided into four different classes, the proposed method produces excellent result
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Sumber metadata: OAI-PMH (metadata jurnal) http://journal.upgris.ac.id/index.php/asset/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
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<dc:title xml:lang="en-US">Milkfish Freshness Classification Using Convolutional Neural Networks Based on Resnet50 Architecture</dc:title>
<dc:creator>Al-Ghiffary, Maulana Malik Ibrahim</dc:creator>
<dc:creator>Sari, Christy Atika</dc:creator>
<dc:creator>Rachmawanto, Eko Hari</dc:creator>
<dc:creator>Yacoob, Noorayisahbe Mohd</dc:creator>
<dc:creator>Cahyo, Nur Ryan Dwi</dc:creator>
<dc:creator>Ali, Rabei Raad</dc:creator>
<dc:subject xml:lang="en-US">milkfish; CNN; ResNet50; Adam Optimizer</dc:subject>
<dc:description xml:lang="en-US">Milkfish (Chanos chanos) had become the main commodity in three major cities in Indonesia, contributed at least 77 thousand tons of aquaculture production in 2021. The quality of fish is determined based on the level of freshness carried out in the sorting process, the sorting process is generally done by evaluating physical characteristics of the fish. However, this method is still considered less efficient and economical because the ability to classify the freshness level of fish can vary for each individual. In this study, by utilizing deep learning, a classification method for milkfish freshness level classification with ResNet50 architecture is proposed, the proposed method is purposed to overcome the previously stated problems, thus creating an efficient and economical system. By creating an efficient system, milkfish sorting process can be carried out quicker and more accurately. Using personal dataset divided into four different classes, the proposed method produces excellent result</dc:description>
<dc:publisher xml:lang="en-US">Universitas PGRI Semarang</dc:publisher>
<dc:contributor xml:lang="en-US">Christy Atika Sari, Eko Hari Rachmawanto, Nur Ryan Dwi Cahyo, Universitas Dian Nuswantoro Semarang</dc:contributor>
<dc:contributor xml:lang="en-US">Noorayisahbe Mohd Yaacob, University of Technology Malaysia (UTM)</dc:contributor>
<dc:contributor xml:lang="en-US">Rabei Raad Ali, Northern Technical University</dc:contributor>
<dc:date>2023-10-31</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-US">Peer-reviewed Article</dc:type>
<dc:format>application/pdf</dc:format>
<dc:identifier>https://journal.upgris.ac.id/index.php/asset/article/view/17017</dc:identifier>
<dc:identifier>10.26877/asset.v5i3.17017</dc:identifier>
<dc:source xml:lang="en-US">Advance Sustainable Science, Engineering and Technology; Vol 5, No 3 (2023): August-October; 0230304</dc:source>
<dc:source>2715-4211</dc:source>
<dc:source>2715-4211</dc:source>
<dc:language>eng</dc:language>
<dc:relation>https://journal.upgris.ac.id/index.php/asset/article/view/17017/pdf</dc:relation>
<dc:rights xml:lang="en-US">Copyright (c) 2023 Advance Sustainable Science Engineering and Technology</dc:rights>
<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>
Sitasi dan pengindeksan
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APA 7
Al-Ghiffary, M. M. I., Sari, C. A., Rachmawanto, E. H., Yacoob, N. M., Cahyo, N. R. D., & Ali, R. R. (2023). Milkfish Freshness Classification Using Convolutional Neural Networks Based on Resnet50 Architecture. Advance Sustainable Science, Engineering and Technology (ASSET), 5(3). https://doi.org/10.26877/asset.v5i3.17017
Salin
IEEE
M. M. I. Al-Ghiffary, C. A. Sari, E. H. Rachmawanto, N. M. Yacoob, N. R. D. Cahyo, and R. R. Ali, “Milkfish Freshness Classification Using Convolutional Neural Networks Based on Resnet50 Architecture,” Advance Sustainable Science, Engineering and Technology (ASSET), vol. 5, no. 3, 2023, doi: 10.26877/asset.v5i3.17017.
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