Optimation Image Classification In Shark With Convolutional Neural Network And Data Augmentation Methods
DOI:
https://doi.org/10.51179/tika.v7i1.993Keywords:
Convolutional Neural Network (CNN), VGG16, Image Classification, Shark, HardAbstract
Sharks are cartilaginous fish that are widely hunted because they have high economic value. Overfishing and trade have resulted in this species being threatened with extinction and have been included in several categories of the IUCN Red List. Information on the types of sharks that landed in Sungailiat Bangka PPN is still very limited due to the difficulty of morphological identification, so it is necessary to identify them using molecular methods. Therefore, the researcher produced an image recognition program in sharks using the Convolutional Neural Network algorithm, which is a convolutional activity by combining several layers of preparation, by utilizing several components that move together and are motivated by a biological sensory system. The shark images used are basking, blacktip, blue, bull, hammerhead, lemon, mako, nurse, sand tiger, and thresher. The implementation of shark image recognition is carried out using 2 test models, namely the Sequential model and the VGG16 on top model running on the Google Collaboratory application, and Keras. The test data in this study were 1089 training data images and 1073 test data images which resulted in an evaluation value with an accuracy value of 86.58% and a loss value of 0.701 on the Sequential model and an accuracy value of 91.80% and a loss value of 0.0355 on the on top model. VGG16.
References
A. Arisandi, A. I N., and S. N.L.G., “Komposisi Ukuran Dan Jenis Kelamin Ikan Hiu Karang Sirip Hitam (Carcharhinus Melanopterus) Komoditas Ekspor Bali,” J. Widya Biol., vol. 11, no. 01, pp. 52–59, 2020, doi: 10.32795/widyabiologi.v11i01.570.
P. P. N. Brondong, L. Jawa, P. Ppn, B. Of, L. Regency, and E. Java, “BIOTROPIKA Journal of Tropical Biology ECOLOGICAL VALUE OF SHARKS CATCHED BY FISHERMAN IN NATIONAL FISHERY,” vol. 8, no. 1, pp. 19–25, 2020.
S. Aisyah, O. Supratman, and A. F. Syarif, “Identifikasi Molekuler Sirip Ikan Hiu Menggunakan Gen Mitokondria Cytochrome C Oxydase Subunit I ( Coi ),” no. July, 2021, doi: 10.31186/jenggano.6.1.CITATIONS.
H. P. Efendi, R. T. Dhewi, and Ricky, “Keragaman Jenis Dan Distribusi Panjang Ikan Hiu di Perairan Selat Makassar,” Pros. Simp. Nas. Hiu Pari Indones., pp. 33–42, 2018.
E. H. Hidayat, S. I. T. Alkadrie, G. M.H, and M. Sabri, “Keberagaman Jenis Ikan Hiu dan Pari di Perairan Kalimantan Barat,” Pros. Simp. Nas. Hiu Pari, no. 2, pp. 89–95, 2018.
M. F. Burhanudin, U. Telkom, and J. Barat, “Integrasi Peran Pada Wisata Hiu : Model Bisnis Ekowisata Daya Tarik Hiu Di Pulau Tinabo Takabonerate Role Integration on Tourism : Business Model of,” Pros. Simp. Nas. Hiu Pari Indones. Ke-2 Tahun 2018, pp. 331–338, 2018.
S. Sutio et al., “Identifikasi Ikan Hiu Yang Tertangkap di Perairan Barat Aceh dan Status Konservasinya Identification and Conservation Status of Sharks Caught in Western Waters of Aceh Province , Indonesia,” vol. 3, no. April 2017, pp. 118–126, 2018.
S. Riyadi and D. I. Mulyana, “Optimasi Image Classification pada Wayang Kulit Dengan Convolutional Neural Network,” vol. 1, no. September 2021, pp. 1–8, 1850.
K. Wisnudhanti and F. Candra, “Image Classification of Pandawa Figures Using Convolutional Neural Network on Raspberry Pi 4,” J. Phys. Conf. Ser., vol. 1655, no. 1, 2020, doi: 10.1088/1742-6596/1655/1/012103.
I. Wulandari, H. Yasin, and T. Widiharih, “Klasifikasi Citra Digital Bumbu Dan Rempah Dengan Algoritma Convolutional Neural Network (Cnn),” J. Gaussian, vol. 9, no. 3, pp. 273–282, 2020, doi: 10.14710/j.gauss.v9i3.27416.
M. A. Hanin, R. Patmasari, and R. Y. Nur, “Sistem Klasifikasi Penyakit Kulit Menggunakan Convolutional Neural Network ( Cnn ) Skin Disease Classification System Using Convolutional Neural Network ( Cnn ),” e-Proceeding Eng., vol. 8, no. 1, pp. 273–281, 2021.
F. I. Kurniadi, “Klasifikasi Topeng Cirebon menggunakan Metode Convolutional Neural Network,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 8, no. 1, pp. 163–169, 2021, doi: 10.35957/jatisi.v8i1.568.
H. Fonda, “Klasifikasi Batik Riau Dengan Menggunakan Convolutional Neural Networks (Cnn),” J. Ilmu Komput., vol. 9, no. 1, pp. 7–10, 2020, doi: 10.33060/jik/2020/vol9.iss1.144.
A. Willyanto, D. Alamsyah, and H. Irsyad, “Identifikasi Tulisan Tangan Aksara Jepang Hiragana Menggunakan Metode CNN Arsitektur VGG-16,” J. Algoritm., vol. 2, no. 1, pp. 1–11, 2021.
R. A. Pangestu, B. Rahmat, and F. T. Anggraeny, “Implementasi Algoritma CNN untuk Klasifikasi Citra Lahan dan Perhitungan Luas,” Inform. dan Sist. Inf., vol. 1, no. 1, pp. 166–174, 2020.
A. Ansor, R. Ritzkal, and Y. Afrianto, “Mask Detection Using Framework Tensorflow and Pre-Trained CNN Model Based on Raspberry Pi,” J. Mantik, vol. 4, no. 3, pp. 1539–1545, 2020, [Online]. Available: http://iocscience.org/ejournal/index.php/mantik/article/view/946.
Y. Hartiwi, E. Rasywir, Y. Pratama, and P. A. Jusia, “Eksperimen Pengenalan Wajah dengan fitur Indoor Positioning System menggunakan Algoritma CNN,” Paradig. - J. Komput. dan Inform., vol. 22, no. 2, pp. 109–116, 2020, doi: 10.31294/p.v22i2.8906.
S. F. Handono, F. T. Anggraeny, and B. Rahmat, “Implementasi Convolutional Neural Network (Cnn) Untuk Deteksi Retinopati Diabetik,” J. Inform. dan Sist. Inf., vol. 1, no. 2, pp. 669–678, 2020, [Online]. Available: http://jifosi.upnjatim.ac.id/index.php/jifosi/article/view/64.
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