CLASSIFICATION OF THREE GENERA OF CORAL FISH USING CONVOLUTIONAL NEURAL NETWORK
Article Sidebar
-
Convolutional Neural Network, coral fish, identification
Abstract
Reef fish are one of the essential organisms in studying coral reef ecosystems, and it is necessary to carry out an identification process to understand the pattern, structure and distribution of reef fish diversity. In addition, reef fish have a vast number and are almost similar to each other. Therefore, to speed up the process of fish identification can be done computerized. One of the automated techniques that can be done is digital image processing. This study aims to classify the image of the genus Fish (Epinephelus spp., Halichoeres spp., and Lutjanus spp.) as economically significant. Image data was obtained from the site https://www.kaggle.com/. The image classification method used is Convolutional Neural Network (CNN) which consists of two stages. The first stage is training with the backpropagation method, and the second stage is image classification using feedforward—the results of the combination of the two methods obtained an accuracy of 85,31%. In addition, the model built is quite good because the average value between precision and sensitivity is not too significant; precision is 89,92%, and sensitivity is 86,49%. Based on the analysis and evaluation that has been done, it can be concluded that the CNN classification method can be appropriately used in classifying fish images by genus.
References
Allken, V., N.O. Handegard, S. Rosen, T. Schreyeck, T. Mahiout, & K. Malde. 2018. Fish species identification using a convolutional neural network trained on synthetic data. ICES Journal of Marine Science, 79(1): 342–349. https://doi.org/10.1093/icesjms/fsy147
Ariawan, I., Y. Herdiyeni, & I.Z. Siregar. 2020. Geometric morphometric analysis of leaf venation in four shorea species for identification using digital image processing. Biodiversitas J. of Biological Diversity, 21(7): 3303–3309. https://doi.org/10.13057/biodiv/d210754
Ariawan, I., Y. Herdiyeni, & I.Z. Siregar. 2022. Geometry feature extraction of shorea leaf venation based on digital image and classification using random forest. International Journal of Computing and Digital Systems, 11(1): 141–150. https://doi.org/10.12785/ijcds/110111
Badawi, U.A. 2022. Fish classification using extraction of appropriate feature set. International Journal of Electrical and Computer Engineering, 12(3): 2488-2500. http://doi.org/10.11591/ijece.v12i3.pp2488-2500
Buchanan, C., Y. Bi, B. Xue, R. Vennell, S. Childerhouse, M.K. Pine, D. Briscoe, & M. Zhang. 2021. Deep convolutional neural networks for detecting dolphin echolocation clicks. Proceeding The 6th International Conference on Image and Vision Computing, Tauranga, New Zealand, 09-10 Desember 2021. 1-6 pp. https://doi.org/10.1109/IVCNZ54163.2021.9653250
Cai, K., X. Miao, W. Wang, H. Pang, Y. Liu, & J. Song. 2020. A modified YOLOv3 model for fish detection based on MobileNetv1 as backbone. Aquacultural Engineering, 91(11): 1-9. https://doi.org/10.1016/j.aquaeng.2020.102117
Chuang, R. & V. Bonhomme. 2019. Rethinking the dental morphological differences between domestic equids. J. of Archaeological Science, 101(1): 140–148. https://doi.org/10.1016/j.jas.2018.02.020
Cisar, P., D. Bekkozhayeva, O. Movchan, M. Saberioon, & R. Schraml. 2021. Computer vision based individual fish identification using skin dot pattern. Scientific Reports, 11(1): 1–12. https://doi.org/10.1038/s41598-021-96476-4
Devarakonda, A., M. Naumov, & M. Garland. 2018. Adabatch: adaptive batch sizes for training deep neural networks. Workshop Track - ICLR, 0(0): 1–4. https://doi.org/10.48550/arXiv.1712.02029
Fauzan, A.R., M.I. Wahyuddin, & S. Ningsih. 2021. Pleural effusion classification based on chest x-ray images using convolutional neural network. Journal of Computer Science and Information, 14(1): 9–16. https://doi.org/10.21609/jiki.v14i1.898
Fitrah, S.S., I. Dewiyanti, T. Rizwan, U. Syiah, & K. Darussalam. 2016. Identifikasi jenis ikan di perairan laguna gampoeng pulot kecamatan leupung aceh besar. Jurnal Ilmiah Mahasiswa Kelautan dan Perikanan Unsyiah, 1(1): 66–81.
Fukushima, K. 1980. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36(4): 193–202. https://doi.org/10.1007/BF00344251
Gao, H., B. Cheng, J. Wang, K. Li, J. Zhao, & D. Li. 2018. Object classification using cnn-based fusion of vision and lidar in autonomous vehicle environment. IEEE Transactions on Industrial Informatics, 14(9): 4224–4230. https://doi.org/10.1109/TII.2018.2822828
Gultom, Y., A.M. Arymurthy, & R.J. Masikome. 2018. Batik Classification using deep convolutional network transfer learning. J. Ilmu Komputer dan Informasi, 11(2): 59–66. https://doi.org/10.21609/jiki.v11i2.507
Han, J., K. Micheline, & P. Jian. 2012. Data mining concepts and techniques. Morgan Kaufman. 364-368 pp.
Hridayami, P., I.K.G.D. Putra, & K.S. Wibawa. 2019. Fish species recognition using VGG16 deep convolutional neural network. Journal of Computing Science and Engineering, 13(3): 124-130. http://dx.doi.org/10.5626/JCSE.2019.13.3.124
Kandel, I. & M. Castelli. 2020. The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset. ICT Express, 6(4): 312–315. https://doi.org/10.1016/j.icte.2020.04.010
Kurniawati, E., V. Siregar, & I.W. Nurjaya. 2020. Classification of shallow water habitat based on object using worldview 2 and sentinel 2b images in kepulauan seribu waters. J. Ilmu dan Teknologi Kelautan Tropis, 12(2): 423–437. https://doi.org/10.29244/jitkt.v12i2.26089
Nashrullah, F., S. Adhi, & G. Budiman. 2020. Investigasi parameter epoch pada arsitektur resnet- 50 untuk klasifikasi pornografi. Journal of Computer, Electronic, and Telecommunication, 1(1): 1–8. https://doi.org/10.52435/complete.v1i1.51
Noija, D., S. Martasuganda., B. Murdiyanto, & A.A. Taurusman. 2014. Pengelolaan sumberdaya ikan kakap merah (Lutjanus spp.) di perairan utara cirebon, laut jawa. Jurnal Teknologi Perikanan dan Kelautan, 5(1): 67-74. https://doi.org/10.24319/jtpk.5.65-74
Putra, I.W.S.E., A.Y. Wijaya, & R. Soelaiman. 2016. Klasifikasi citra menggunakan convolutional neural network (cnn) pada caltech 101. Jurnal Teknik ITS, 5(1): A65–A69. https://doi.org/10.12962/j23373539.v5i1.15696
Rauf, H.T., M.I.U. Lali, S. Zahoor, S.Z.H. Shah, A.U. Rehman, & S.A.C. Bukhari. 2019. Visual features based automated identification of fish species using deep convolutional neural networks. Computers and Electronics in Agriculture, 167(Desember): 1-17. https://doi.org/10.1016/j.compag.2019.105075
Rokhana, R., J. Priambodo, T. Karlita, I.M.G. Sunarya, E.M. Yuniarno, I.K.E. Purnama, & M.H. Purnomo. 2019. Convolutional neural network untuk pendeteksian patah tulang femur pada citra ultrasonik b–mode. J. Nasional Teknik Elektro Dan Teknologi Informasi, 8(1): 59–67. https://doi.org/10.22146/jnteti.v8i1.491
Rondonuwu, A.B., 2014. Ikan karang di wilayah terumbu karang kecamatan maba kabupaten halmahera timur provinsi maluku utara. Jurnal Ilmiah Platax, 2(1).
https://doi.org/10.35800/jip.2.1.2014.4403
Santra, A.K. & C.J. Christy. 2012. Genetic algorithm and confusion matrix for document clustering. International J. Of Computer Science Issues, 9(1): 322–328. http://ijcsi.org/papers/IJCSI-9-1-2-322-328.pdf
Shammi, S.A., S. Das, & M.M. Hasan. 2021. FishNet: fish classification using convolutional neural network. Proceeding The 12th International Conference on Computing Communication and Networking Technologies, Kharagpur, India, 06-08 Juli 2021. 1-6 pp. https://doi.org/10.1109/ICCCNT51525.2021.9579550
Sugara, A., A.Nolisa, A. Anggoro, A.N.N Suci, R.T. Utami, Y. Andika, F. Nugroho, & R. Suhendri. 2022. Identifikasi keanekaragaman jenis ikan hasil tangkapan nelayan tapak paderi kota bengkulu. Samakia: Jurnal Ilmu Perikanan, 13(1): 51-62. https://doi.org/10.35316/jsapi.v13i1.1664
Sukarman, L.D., G.F. Laxmi, & F. Fatimah. 2018. Identifikasi ikan air tawar dengan metode color moment feature. Proceedings seminar nasional teknologi informasi, Bogor, 3 Mei 2018. 375–383 pp.
Xu, X., W. Li. & Q. Duan. 2021. Transfer learning and SE-ResNet152 networks-based for small-scale unbalanced fish species identification. Computers and Electronics in Agriculture. 180(1): 1-9. https://doi.org/10.1016/j.compag.2020.105878
Ying, X. 2019. An overview of overfitting and its solutions. Journal of Physics: Conference Series, 1–6. https://doi.org/10.1088/1742-6596/1168/2/022022
Authors
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
The author submitting the manuscript must understand and agree that the copyright of the article manuscript must be submitted/transferred to the Jurnal Ilmu dan Teknologi Kelautan Tropis. This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA) International License in which the Author and Reader can copy and redistribute the material in any media or format, and remix, modify and build material for any purpose, but they must provide appropriate credit (citing articles or content), provide a link to the license, and indicate whether there is a change. If you mix, change, or create material, you must distribute your contribution under the same license as the original.
