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All Journal Pendas : Jurnah Ilmiah Pendidikan Dasar Jurnal Pendidikan Matematika (JUDIKA EDUCATION) JURNAL PENDIDIKAN TAMBUSAI COLLASE (Creative of Learning Students Elementary Education) Neraca Jurnal Ilmu Hukum, Humaniora dan Politik (JIHHP) Journal of Artificial Intelligence and Digital Business Jurnal Manajemen Dan Akuntansi Medan Sahmiyya: Jurnal Ekonomi dan Bisnis Jurnal Interaksi Sosiologi Riwayat: Educational Journal of History and Humanities JRIIN :Jurnal Riset Informatika dan Inovasi Jurnal Akselerasi Merdeka Belajar dalam Pengabdian Orientasi Masyarakat (Jurnal AMPOEN) Satmata: Journal or Historical Education Studies JER Journal of Fisheries & Marine Al-HIbru: Jurnal Ilmu Bahasa Arab dan Pembelajarannya Akhlak: Jurnal Pendidikan Agama Islam Dan Filsafat Journal of International Accounting, Taxation and Information Systems Parabela: Jurnal Ilmu Pemerintahan & Politik Lokal
Machruf, Awalludin
Management Study Program, Faculty Of Business Economics And Digital Technology, Nahdlatul Ulama University Surabaya
Religion Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Journal : Journal of Fisheries

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Deep Learning Models Performance on Marine Fish Species Classification Awalludin, Ezmahamrul Afreen; Anang, Nur Muhammad Afiq
Jurnal Ilmiah Perikanan dan Kelautan Vol. 17 No. 3 (2025): JURNAL ILMIAH PERIKANAN DAN KELAUTAN
Publisher : Faculty of Fisheries and Marine Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jipk.v17i3.71815

Abstract

Graphical Abstract   Highlight Research The ResNet50 presented the highest accuracy for classifying 20 marine fish species in the study. The performance comparison demonstrated that ResNet50 outperformed both AlexNet and GoogLeNet. Transfer learning enabled effective feature extraction from limited datasets. Deep learning models offer potential for automating the classification of marine fish     Abstract Identifying marine fish species accurately can be difficult due to their subtle anatomical and colour pattern similarities, which often result in misclassification during ecological assessments and fisheries operations. Manual identification methods are time-consuming and prone to errors especially in high throughput environments such as fish markets. In this study, transfer learning is used to evaluate three deep learning models ResNet-50, AlexNet and GoogLeNet on a total of 20,325 images from twenty marine fish species acquired from Kuantan (Pahang) and Mengabang Telipot (Kuala Nerus), Malaysia. All images were morphologically classified as complete fish, head, body and tail. The dataset was subjected to preprocessing procedures which encompassed image resizing, pixel normalization and data augmentation techniques that consists of random rotation (±15°), horizontal flipping, adjustments to brightness and contrast (±20%) and cropping. Subsequently, the dataset was partitioned into 80% training set (16,260 images), 10% validation set (2,032 images) and 10% testing set (2,033 images). The classification patterns were analysed using confusion matrices and standard metrics such as accuracy, precision and recall. ResNet-50 outperformed other models achieving ideal results with 100% accuracy, precision and recall in every category. With 99.5% and 99.4% accuracy, GoogleNet and AlexNet came in second and third, respectively. This study shows that deep learning models especially ResNet-50 achieved an accurate and efficient way to classify fish species automatically. With multi-view images, data augmentation and transfer learning, the model performs well even in difficult visual conditions. These results support its use in real-time fisheries monitoring, biodiversity studies, and environmental impact assessments
A Comprehensive Review of Deep Learning Approaches in Fish Classification Using Convolutional Neural Networks From 2015 to October 2025 Nur Muhammad Afiq Anang; Awalludin, Ezmahamrul Afreen
Jurnal Ilmiah Perikanan dan Kelautan 2026: JIPK VOLUME 18 ISSUE 2 YEAR 2026 (JUNE 2026, ISSUE IN PROGRESS)
Publisher : Faculty of Fisheries and Marine Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jipk.v18i2.79656

Abstract

Graphical Abstract Highlight Research 1. Provide insights into the development, methodologies and significant advancements in fish classification research utilizing CNNs. 2. The review discusses the progression of CNN architectures, beginning with earlier models such as AlexNet and advancing to more sophisticated frameworks like ResNet and GoogLeNet. 3. Examines the extensive use of benchmark datasets across many studies and discusses collaborative research efforts that refine models and enhance reproducibility. 4. Guide future research by underscoring best practices, acknowledging less explored areas and encouraging interdisciplinary approaches to monitoring fish biodiversity and managing aquatic ecosystems through deep learning.   Abstract Recent advancements in deep learning have substantially improved the classification of fish species. These innovations present a contemporary and dependable alternative to conventional methodologies, such as image processing and manual identification. Implementing Convolutional Neural Networks (CNNs) has significantly enhanced accuracy, flexibility and scalability within aquatic ecosystems. This comprehensive review evaluates 81 scholarly articles published from January 2015 to October 2025. The analyses were guided by the VICO framework, supported by defined inclusion and exclusion criteria, data extraction and synthesis and the PRISMA process to ensure systematic selection of relevant studies. It aims to provide insights into the development, methodologies, and significant advancements in fish classification research utilizing CNNs. The review discusses the progression of CNN architectures, beginning with earlier models such as AlexNet and advancing to more sophisticated frameworks like ResNet and GoogLeNet, as well as transformer and hybrid CNN model such as the Vision Transformer (ViT) and the Convolutional Vision Transformer (ConViT). It highlights the adoption rates, training performance and contexts of use. Additionally, it examines the extensive use of benchmark datasets across many studies and discusses collaborative research efforts that refine models and enhance reproducibility. A thorough comparison of classification accuracy, dataset composition and trends in model choice offers a clearer picture of the current impact of deep learning in this area. Furthermore, this review identifies crucial challenges, including the lack of data for rare species, issues related to low-resolution image recognition and the need for standardization in model evaluation. The insights offered aim to guide future research by underscoring best practices, acknowledging less explored areas and encouraging interdisciplinary approaches to monitoring fish biodiversity and managing aquatic ecosystems through deep learning.
Co-Authors Abdiyanto Abdiyanto Abidatun Mu’awanah, Nurul adisti, tria Ahmad Maftuh Sujana Akbar, Fatih Hidayat Alfian, fiqri Alya Citra Salsabila Amanda, Suci Anang, Nur Muhammad Afiq Asdar Maknung, Muh. Awalludin, Subhan Ajiz Bagja Maulana Burhannurdin Nabih Kurniawan Chaiyun Nafiah, Ilmi Darwis, Muhajir Eka Pratama, Ahmad Yudha Elsya Wahyuni Exabiaggi, Rizal Dhani Farida Rohmah Fatimah Az Zahra Harianja, Callysta Marcella Hasin, Farid Bima Hayu Maharani, Dama Gandes Helman Manay, Helman Hilmi Al Hazmi Hidaya Janah, Nuryatul khairunisa, Nuke Ferli Khairunisa Kurniawati Maulidya, Raisya Ayu Mitro Subroto Muhammad Rifal Nurfalah Mulyanty, Dety Munandar, Eris Nur Muhammad Afiq Anang Nurhidayat, Rifky Nurmalya Kardina, Rizki Padilillah, Rahma Ramdani, Alif Dimas Riyan Sisiawan Putra Rusmana, Apit Sabrina, Laila Sadali, Ayatullah Saidah, Wirdati Santika, Ganjar Sari, Eka Anisa SITI MARYAM Soetji Lestari Sri Wahyuni Suci Sulistiawati Sucitra Dinika Akbar Sutrisno Mohamad Tamamudin Tria Adisti Ariyani, Amalia Rahmawati Verry Ferdian Putra Widia Ningsih, Widia Wijayanti, Lono