Deteksi Ikan Dengan Menggunakan Algoritma Histogram of Oriented Gradients

Fetty Tri Anggraeny, Basuki Rahmat, Singgih Putra Pratama

Abstract


Indonesia merupakan negara yang kaya akan sumber daya alam baik hayati maupun non-hayati. Salah satu sumber daya alam hayati yang sangat banyak jumlahnya di Indonesia adalah laut, Untuk mempermudah mengidentifikasikan ikan, dapat memanfaatkan sebuah teknologi yang dapat membantu manusia untuk dapat mengenali ikan dengan menggunakan visi komputer dan pendekatan pemrosesan gambar untuk deteksi ikan dan bukan ikan menggunakan algoritma Histogram of Oriented Gradients (HOG) dan AdaBoost-SVM. Hasil penelitian menunjukkan bahwa metode HOG dan AdaBoost-SVM dapat menghasilkan tingkat akurasi rata-rata sebesar 84.8%.


Keywords


Deteksi Ikan; Histogram of Oriented Gradients; Adaboost-SVM;

Full Text:

PDF

References


Dalal, N., & Triggs, B. (2005). Histograms of Oriented Gradients for Human Detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). https://doi.org/10.1109/CVPR.2005.177

Deng, Z., Cao, M., Rai, L., & Gao, W. (2018). A two-stage classification method for borehole-wall images with support vector machine. PLoS ONE, 13(6), 1–19. https://doi.org/10.1371/journal.pone.0199749

Freund, Y., & Schapire, R. E. (1999). A short introduction to bosting. Journal of Japanese Society for Artificial Intelligence, 14(5), 771–780. Retrieved from https://cseweb.ucsd.edu/~yfreund/papers/IntroToBoosting.pdf

Fu, L., Duan, J., Zou, X., Lin, G., Song, S., Ji, B., & Yang, Z. (2019). Banana detection based on color and texture features in the natural environment. Computers and Electronics in Agriculture, 167(July), 105057. https://doi.org/10.1016/j.compag.2019.105057

Irawan, F., Purnomo, A., & Alamsyah, D. (2015). Deteksi Mobil pada Citra Digital Menggunakan C-HOG dan Support Vector Machine. GI MDP & MDP BUSINESS Journal, (x), 1–12.

Kumaseh, M. R., Latumakulita, L., & Nainggolan, N. (2013). Segmentasi Citra Digital Ikan Menggunakan Metode Thresholding. Jurnal Ilmiah Sains, 13(1), 74. https://doi.org/10.35799/jis.13.1.2013.2057

Li, X., Shang, M., Qin, H., & Chen, L. (2016). Fast accurate fish detection and recognition of underwater images with Fast R-CNN. OCEANS 2015 - MTS/IEEE Washington, 1–5. https://doi.org/10.23919/oceans.2015.7404464

Melville, P., & Mooney, R. J. (2005). Creating diversity in ensembles using artificial data. Information Fusion, 6(1), 99–111. https://doi.org/10.1016/j.inffus.2004.04.001

Otsu, N. (1979). A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66. https://doi.org/10.1109/TSMC.1979.4310076

Permata, C., & Eddy, I. K. (2012). Deteksi Mobil Menggunakan Histogram of Oriented Gradient. Institut Teknologi Sepuluh Nopember. Retrieved from http://repository.its.ac.id/3084/

Pratama, S. P., Rahmat, B., & Anggraeny, F. T. (2020). Deteksi Ikan Dengan Menggunakan Algoritma Adaboost-SVM. Jurnal Informatika Dan Sistem Informasi (JIFoSI), 1(2), 458–466. Retrieved from http://jifosi.upnjatim.ac.id/index.php/jifosi/article/view/92/64

Prianto, E., & Suryanti, N. K. (2010). Komposisi jenis dan potensi sumber daya ikan di muara Sungai Musi. Jurnal Penelitian Dan Perikanan Indonesia, 16(1), 1–8. Retrieved from file:///C:/Users/User/Downloads/3393-8613-1-SM.pdf

Purbasari, Intan Y Anggraeny, F. T., & Harianto, N. (2018). Classification of broiler chicken eggs using support vector machine (svm) and feature selection algorithm. ICITB Proceedings.

Salman, A., Maqbool, S., Khan, A. H., Jalal, A., & Shafait, F. (2019). Real-time fish detection in complex backgrounds using probabilistic background modelling. Ecological Informatics, 51(February), 44–51. https://doi.org/10.1016/j.ecoinf.2019.02.011

Santoso, S. J., Setiyono, B., & Isnanto, R. R. (2011). Pengenalan jenis-jenis ikan menggunakan metode analisis komponen utama. Universitas Diponegoro. Retrieved from http://eprints.undip.ac.id/25746/

Syafi’i, S. I., Wahyuningrum, R. T., & Muntasa, A. (2016). Segmentasi Obyek Pada Citra Digital Menggunakan Metode Otsu Thresholding. Jurnal Informatika, 13(1), 1–8. https://doi.org/10.9744/informatika.13.1.1-8

Tri Anggraeny, F., & Yuniar Purbasari, I. (2019). Jaringan Syaraf Tiruan dan Modifikasinya Menggunakan Supervised Learning. Surabaya: Indomedia Pustaka.




DOI: http://dx.doi.org/10.30872/jim.v15i2.4648

Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer

Editor Informatika Mulawarman Address:
ISSN 1858-4853 (Print) | ISSN 2597-4963 (Online)
Published by: Mulawarman University
Managed by : Informatika Department
Jalan Sambaliung No.9 Sempaja Selatan Samarinda Utara,
Kalimantan Timur 75117 - Indonesia
E-mail: jim.unmul@gmail.com
OJS: http://e-journals.unmul.ac.id/index.php/JIM
Contact Person: Gubtha Mahendra Putra

 Creative Commons License

Informatika Mulawarman by http://e-journals.unmul.ac.id/index.php/JIM/index is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Under the CC BY-SA license, authors and other users are able to reprint, distribute or use the material for commercial purposes so long as they give attribution to the journal Informatika Mulawarman and license the republished material under the same license.