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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) INTECOMS: Journal of Information Technology and Computer Science JSAI (Journal Scientific and Applied Informatics) Informanika Jurnal Pengabdian Kepada Masyarakat Inovasi Teknologi Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Digital dan Rekayasa Informatika (JARDIRA) Journal of Engineering and Technology Innovation
Lemi Iryani
Universitas Sjakhyakirti
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Transportation Other

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Klasifikasi Spesies Ikan di Sumatera Selatan Berdasarkan Citra Bawah Air Menggunakan ResNet-50 Lemi Iryani; Nia Umilizah; Firga Abel Astiawan; Muhammad Al Hapiz
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.9389

Abstract

Fish species classification in underwater ecosystems posed a significant challenge, particularly due to poor lighting that affected the quality of underwater images and decreased the accuracy of species identification. This study aimed to improve the accuracy of fish species classification in South Sumatra based on underwater images by utilizing the Super-Resolution Generative Adversarial Network (SRGAN) to enhance image quality and ResNet-50 for species classification. The research employed a Dell XPS 13 9310 device with an Intel Core i7 processor and 16GB of RAM for model training. Fish image data were collected from Google Images and YouTube according to predefined fish species, including Oreochromis mossambicus (Mujair), Oreochromis niloticus (Nila), Johnius trachycephalus (Gulamah), Eleutheronema tetradactylum (Senangin), and Chanos chanos (Bandeng). The data was divided into 70% for training, 15% for validation, and 15% for testing. The experimental results showed that the developed model achieved a training accuracy of 94.10%, validation accuracy of 88.25%, and testing accuracy of 84.68%. This research contributed to the field of underwater image classification and can be applied to conservation and monitoring of fish species in aquatic ecosystems.
Co-Authors Ade Sukma Wati Agrinda Ghaniya Andika Bakhtiar K Bakhtiar, Bakhtiar. Darmo, Cahyo Pambudi Dwi Fitri Brianna Firga Abel Astiawan Junaidi Junaidi Mariana Purba Mariana Purba Muhammad Al Hapiz Nia Umilizah Nia Umilizah Nur Ani Octavia, Pipin Putri Maharani Rudiansyah Rudiansyah Syamsiar, Syamsiar