DOI: https://doi.org/10.14710/vol15iss2pp173-178

Identification of Grouper Fish Types using Convolutional Neural Network Resnet-50 Algorithm

*Rini Nuraini  -  FTKI, Universitas Nasional, Jl. Sawo Manila No.61, Pejaten, Jakarta, Indonesia 12510 , Indonesia
Wahyul Amien Syafei  -  Electrical Engineering, Faculty of Engineering, Diponegoro University, Jl. Prof. H. Soedarto, S.H., Tembalang, Semarang, Indonesia 50275, Indonesia
Adi Wibowo  -  Informatics Department, Faculty of Science and Mathematics, Diponegoro University, Jl. Prof. H. Soedarto, S.H., Tembalang, Semarang, Indonesia 50275, Indonesia
Indra Jaya  -  Faculty of Fisheries and Maritime Affairs, Institut Pertanian Bogor, Kampus IPB Dramaga, Jl. Agatis, Dramaga, Kec. Dramaga, Bogor, Indonesia 16680, Indonesia
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Abstract

Grouper is a type of fish that is popular with the public. It is necessary to identify the type of grouper fish based on color patterns with increase the epoch value to get the best accuracy. The purpose of the research is to predict the type of grouper. This research use CNN Resnet-50 algorithm. 30 data used. The accuracy of prediction is 75 % to predict the image groupers. In the grouper prediction process, the more we increase the epoch value, we will get the best accuracy value. Epoch is a factor that affects the time of training an AI model and affects the accuracy value of the AI model.

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Keywords: Grouper Fish; CNN Resnet-50; Epoch Value; Image Classification; Deep Learning

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Section: Research Articles
Language : EN
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  1. A. B. J. Malyan, R. R. Putra, E. Laila, A. T. Wardhana, M. Fikri, and I. G. T. Isa, Epoch Tuning Hyperparameter in Fire Image Classification at University Sjakhyakirti, no. July. Atlantis Press International BV, 2023
  2. A. Sharma, Convolutional Neural Networks in Python, 2018
  3. C. Liu et al., “Research progress of computer vision technology in abnormal fish detection,” Aquac. Eng., vol. 103, no. May, 2023, doi: 10.1016/j.aquaeng.2023.102350
  4. D. Attenborough, “in Blue Planet II series 1:3 Coral Reefs,” BBC, 2017. Available: https://www.bbc.co.uk/iplayer/episode/b09g4d98/blue-planet-ii-series-1-3-coral-reefs
  5. F. F. Pramesti, L. Sulmartiwi, and S. Andriyono, “Molecular Identification of Grouper Fish (Perciformes: Serranidae) Landed From Pangpang Bay, Banyuwangi,” J. Trop. Mar. Sci., vol. 5, no. 2, pp. 98–103, 2022, doi: 10.33019/jour.trop.mar.sci.v5i2.2955
  6. H. T. Rauf, M. I. U. Lali, S. Zahoor, S. Z. H. Shah, A. U. Rehman, and S. A. C. Bukhari, “Visual features based automated identification of fish species using deep convolutional neural networks,” Comput. Electron. Agric., vol. 167, no. July, 2019, doi: 10.1016/j.compag.2019.105075
  7. J. Gladju, B. S. Kamalam, and A. Kanagaraj, “Applications of data mining and machine learning framework in aquaculture and fisheries: A review,” Smart Agric. Technol., vol. 2, no. April, p. 100061, 2022, doi: 10.1016/j.atech.2022.100061
  8. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 770–778, 2016, doi: 10.1109/CVPR.2016.90
  9. M. Kubat, I. Bratko, and R. S. Michalski, “A Review of Machine Learning Methods,” Mach. Learn. Data Min. Methods Appl., vol. 12, no. Ker 12101, pp. 1–72, 1996
  10. O. U. Press, Oxford Dictionary: “grouper.” Oxford University Press, 2016
  11. R. Bshary, A. Hohner, K. Ait-el-Djoudi, and H. Fricke, “Interspecific communicative and coordinated hunting between groupers and giant moray eels in the red sea,” PLoS Biol., vol. 4, no. 12, pp. 2393–2398, 2006, doi: 10.1371/journal.pbio.0040431
  12. R. H. Robin, “Epinephelus itajara,” Discover Fish. Florida Museum, 2020. Available: https://www.floridamuseum.ufl.edu/discover-fish/species-profiles/epinephelus-itajara/
  13. S. B. Kotsiantis, I. D. Zaharakis, and P. E. Pintelas, “Machine learning: A review of classification and combining techniques,” Artif. Intell. Rev., vol. 26, no. 3, pp. 159–190, 2006, doi: 10.1007/s10462-007-9052-3
  14. S. H. Liao, P. H. Chu, and P. Y. Hsiao, “Data mining techniques and applications - A decade review from 2000 to 2011,” Expert Syst. Appl., vol. 39, no. 12, pp. 11303–11311, 2012, doi: 10.1016/j.eswa.2012.02.063
  15. T. A. Santosa, W. A. Fietri, A. Razak, and R. Sumarmin, “Phylogenetic analysis of the grouper family (Serranidae) from various local markets in Indonesian waters using COI (cytochrome oxidase I),” Edubiotik J. Pendidikan, Biol. dan Terap., vol. 6, no. 01, pp. 74–82, 2021, doi: 10.33503/ebio.v6i01.1204

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