Pengenalan Spesies Ikan Berdasarkan Kontur Otolith Menggunakan Convolutional Neural Network
Abstract
Hasil sensus kehidupan laut pada tahun 2013 di seluruh dunia terdapat lebih dari 23.000 spesies dan masih banyak sekali spesies ikan yang belum diidentifikasi. Otolith merupakan organ yang sangat penting di belakang telinga ikan, karena melalui otolith ini dapat diketahui jenis ikan, pertumbuhan dan lingkungan, serta sejarah kehidupannya, misalnya, umur, reproduksi, dan migrasi. Dengan semakin canggihnya komputer dan pengolahan di bidang citra, diharapkan kemampuan mengidentifikasi jenis ikan yang dimiliki oleh manusia bisa diadopsi dan diterapkan pada perangkat komputer. Deep Learning saat ini semakin berkembang memanfaatkan sumber daya perangkat keras yang semakin canggih termasuk penggunaan GPU (Graphical Processing Unit) untuk perhitungan proses komputasi dengan akurasi yang lebih baik dan proses yang lebih cepat. Pada penelitian ini metode yang diusulkan, untuk keperluan klasifikasi ikan menggunakan metode Convolutional Neural Network dengan teknik Transfer Learning dari model Alexnet dan optimasi Momentum Stochastic Gradient Descent. Hasil eksperimen diperoleh akurasi sebesar 95.4% lebih tinggi dibanding metode Discriminant Analysis yang memiliki akurasi sebesar 92%.
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Campana, S. (1999). Chemistry and composition of fish otoliths:pathways, mechanisms and applications. Marine Ecology Progress Series, 188, 263–297. https://doi.org/10.3354/meps188263
Chang, J., & Park, E. (2017). A Method for Classifying Medical Images using Transfer Learning?: A Pilot Study on Histopathology of Breast Cancer, 2015–2018.
Convolutional Neural Network - Documentation. (n.d.). Retrieved March 3, 2018, from http://deeplearning.net/tutorial/lenet.html
Convolutional Neural Network (CNN). (n.d.). Retrieved March 1, 2018, from http://i-systems.github.io/HSE545/machine learning all/Workshop/KSME/04_KSME_CNN.html
CS231n Convolutional Neural Networks for Visual Recognition. (n.d.). Retrieved March 1, 2018, from http://cs231n.github.io
Deep Learning 101 - Part 2: Multilayer Perceptrons. (n.d.). Retrieved March 1, 2018, from https://beamandrew.github.io/deeplearning/2017/02/23/deep_learning_101_part2.html
Fajar, A. (2013). Data Mining. Yogyakarta: Andi Offset Yogyakarta.
Fitch, B. J. E., & Brownell, R. L. (1968). Fish Otoliths frnportance Cetacean Stornachs and Their Feeding Habitsl fnterpreting, 25(12), 2561–2574.
Kalish, J. M. (1991). Oxygen and carbon stable isotopes in the otoliths of wild and laboratory-reared Australian salmon (Arripis trutta). Marine Biology, 110, 37–47.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances In Neural Information Processing Systems, 1–9. https://doi.org/http://dx.doi.org/10.1016/j.protcy.2014.09.007
Kubat, M. (2017). An Introduction to Machine Learning. https://doi.org/10.1007/978-3-319-63913-0
Le, H. T., Urruty, T., Beurton-aimar, M., Nghiem, T. P., Tran, H. T., Verset, R., … Visani, M. (2018). Study of CNN Based Classification for Small Specific Datasets. https://doi.org/10.1007/978-3-319-76081-0
Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Lecun, Y., Bottou, L., & Bengio, Y. (1998). Gradient-based learning applied to document recognition.
Lemaréchal, C. (2012). Cauchy and the Gradient Method. Documenta Mathematica, ISMP, 251–254. Retrieved from https://www.math.uni-bielefeld.de/documenta/vol-ismp/40_lemarechal-claude.pdf
Leon Bottou. (2010). Large-Scale Machine Learning with Stochastic Gradient Descent. https://doi.org/10.1007/978-3-7908-2604-3
Loizou, N., & Richtárik, P. (2017). Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods. Retrieved from http://arxiv.org/abs/1712.09677
Lombarte, A., & Castellón, A. (1991). Interspecific and intraspecific otolith variability in the genus Merluccius as determined by image analysis. Can. J. Zool., 69, 2442–2449. https://doi.org/10.1139/z91-343
Mathworks. (2017). Introducing Deep Learning with Matlab.
Muhammad Arhami, S.Si., M. K. (2006). Konsep Kecerdasan Buatan. Yogyakarta: Andi Publisher.
Neural Network architectures. (n.d.). Retrieved February 27, 2018, from http://cs231n.github.io/neural-networks-1/
Parisi-Baradad, V., Manjabacas, A., Lombarte, A., Olivella, R., Chic, Ò., Piera, J., & García-Ladona, E. (2010). Automated Taxon Identification of Teleost fishes using an otolith online database-AFORO. Fisheries Research, 105(1), 13–20. https://doi.org/10.1016/j.fishres.2010.02.005
Qian, N. (1999). On the momentum term in gradient descent learning algorithms. Neural Networks, 12(1), 145–151. https://doi.org/10.1016/S0893-6080(98)00116-6
Rafael C. Gonzalez, & Woods, R. E. (2002). Digital Image Processing (2nd ed.). Prentice Hall.
Reichenbacher, B., Sienknecht, U., Kuchenhoff, H., & Fenske, N. (2007). Combined Otolith Morphology and Morphometry for Assessing Taxonomy and Diversity in Fossil and Extant Killifish (Aphanius, yProlebias). Journal of Morphology, 268(February), 254–274. https://doi.org/10.1002/jmor
Roumillat, W. A. (2004). Manual of Fish Sclerochronology. Copeia, 2004(1), 195–195. https://doi.org/10.1643/OT-03-266
Salimi, N., Loh, K. H., Kaur Dhillon, S., & Chong, V. C. (2016). Fully-automated identification of fish species based on otolith contour: using short-time Fourier transform and discriminant analysis (STFT-DA). PeerJ, 4, e1664. https://doi.org/10.7717/peerj.1664
Siddiqui, S. A., Salman, A., Malik, M. I., Shafait, F., Mian, A., Shortis, M. R., & Harvey, E. S. (2018). Automatic fish species classification in underwater videos: Exploiting pre-trained deep neural network models to compensate for limited labelled data. ICES Journal of Marine Science, 75(1), 374–389. https://doi.org/10.1093/icesjms/fsx109
Soria, A., & Baradad, P. (2012). On the Automatic Detection of Otolith Features for Fish Species Identification and their Age Estimation.
The Secret Lives of Fish: Cohen Lab. (n.d.). Retrieved April 1, 2018, from http://www.whoi.edu/page.do?pid=130656&tid=3622&cid=2466
Thomas, O. R. B., Ganio, K., Roberts, B. R., & Swearer, S. E. (2017). Trace element–protein interactions in endolymph from the inner ear of fish: implications for environmental reconstructions using fish otolith chemistry. Metallomics, 9(3), 239–249. https://doi.org/10.1039/C6MT00189K
Published
2019-07-04
How to Cite
DARMANTO, Heri.
Pengenalan Spesies Ikan Berdasarkan Kontur Otolith Menggunakan Convolutional Neural Network.
Joined Journal (Journal of Informatics Education), [S.l.], v. 2, n. 1, p. 41-59, july 2019.
ISSN 2620-8415.
Available at: <https://www.e-journal.ivet.ac.id/index.php/jiptika/article/view/847>. Date accessed: 05 july 2025.
doi: https://doi.org/10.31331/joined.v2i1.847.
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