Combination of Feature Extractions for Classification of Coral Reef Fish Types Using Backpropagation Neural Network

Luther Latumakulita - Sam Ratulangi University, North Sulawesi, Indonesia
I Nyoman Arya Astawa - Politeknik Negeri Bali, Bali, Indonesia
Vitrail Mairi - Sam Ratulangi University, North Sulawesi, Indonesia
Fajar Purnama - Udayana University, Bali, Indonesia
Aji Wibawa - Universitas Negeri Malang, Malang, East Java, Indonesia
Nida Jabari - Palestine Technical University Kadoorie, Palestine
Noorul Islam - Kanpur Institute of Technology, Kanpur, India

Citation Format:



Feature extraction is important to obtain information in digital images, where feature extraction results are used in the classification process. The success of a study to classify digital images is highly dependent on the selection of the feature extraction method used, from several studies providing a combination of feature extraction solutions to produce a more accurate classification.  Classifying the types of marine fish is done by identifying fish based on special characteristics, and it can be through a description of the shape, fish body pattern, color, or other characteristics. This study aimed to classify coral reef fish species based on the characteristics contained in fish images using Backpropagation Neural Network (BPNN) method. Data used in this research was collected directly from Bunaken National Marine Park (BNMP) in Indonesia. The first stage was to extract shape features using the Geometric Invariant Moment (GIM) method, texture features using Gray Level Co-occurrence Matrix (GLCM) method, and color feature extraction using Hue Saturation Value (HSV) method. The third value of feature extraction was used as input for the next stage, namely the classification process using the BPNN method. The test results using 5-fold cross-validation found that the lowest test accuracy was 85%, the highest was 100%, and the average was 96%. This means that the intelligent model derived from the combination of the three feature extraction methods implemented in the BPNN training algorithm is very good for classifying coral reef fish.


BPNN; classification; combination method; coral reef fish; feature extraction.

Full Text:



N. Jisr, G. Younes, C. Sukhn, and M. H. El-Dakdouki, "Length-weight relationships and relative condition factor of fish inhabiting the marine area of the Eastern Mediterranean city, Tripoli-Lebanon," The Egyptian Journal of Aquatic Research, vol. 44, pp. 299-305, 2018/12/01/ 2018.

A. Vatresia;, R. Faurina;, V. Purnamasari;, and I. Agustian, "Automatic Fish Identification Using Single Shot Detector," COMMIT Journal, vol. 16, 2022.

X. Zhai, X. Chu, C. S. Chai, M. S. Y. Jong, A. Istenic, M. Spector, J.-B. Liu, J. Yuan, and Y. Li, "A Review of Artificial Intelligence (AI) in Education from 2010 to 2020," Complexity, vol. 2021, p. 8812542, 2021/04/20 2021.

A. Sayadi, M. Monjezi, N. Talebi, and M. Khandelwal, "A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak," Journal of Rock Mechanics and Geotechnical Engineering, vol. 5, pp. 318-324, 2013/08/01/ 2013.

L. A. Latumakulita and T. Usagawa, "Indonesia Scholarship Selection Model Using a Combination of Back-Propagation Neural Network and Fuzzy Inference System Approaches," International Journal of Intelligent Engineering and Systems, vol. 11, pp. 79-90, 2018.

M. K. S. Alsmadi, K. B. Omar, S. A. M. Noah, and I. Almarashdeh, "Fish recognition based on robust features extraction from color texture measurements using backpropagation classifier," Journal of theoretical and applied information technology, vol. 18, pp. 11-18, 2010.

U. Andayani, A. Wijaya, R. F. Rahmat, B. Siregar, and M. F. Syahputra, "Fish Species Classification Using Probabilistic Neural Network," Journal of Physics: Conference Series, vol. 1235, p. 012094, 2019/06/01 2019.

Prasetiyo, M. Khalid, R. Yusof, and F. Meriaudeau, "A Comparative Study of Feature Extraction Methods for Wood Texture Classification," in 2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems, 2010, pp. 23-29.

A. Baruah and L. P. Saikia, "Study and Analysis of Different Feature Extraction Methods in Digital Image Processing," 2020.

W. A. Saputra and D. Herumurti, "Integration GLCM and geometric feature extraction of region of interest for classifying tuna," in 2016 International Conference on Information & Communication Technology and Systems (ICTS), 2016, pp. 75-79.

F. S. Lesiangi, A. Y. Mauko, and B. S. Djahi, "Feature extraction Hue, Saturation, Value (HSV) and Gray Level Cooccurrence Matrix (GLCM) for identification of woven fabric motifs in South Central Timor Regency," Journal of Physics: Conference Series, vol. 2017, p. 012010, 2021/09/01 2021.

A. Subasi, "Chapter 4 - Feature Extraction and Dimension Reduction," in Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques, A. Subasi, Ed., ed: Academic Press, 2019, pp. 193-275.

E. Dougherty, Mathematical Morphology in Image Processing. New York: Marcel Dekker, 2018.

K. Joshi and B. Patil, "Prediction of Surface Roughness by Machine Vision using Principal Components based Regression Analysis," Procedia Computer Science, vol. 167, pp. 382-391, 2020/01/01/ 2020.

M. Pérez, M. E. Benalcázar, E. Tusa, W. Rivas, and A. Conci, "Mammogram classification using backpropagation neural networks and texture feature descriptors," in 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM), 2017, pp. 1-6.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

JOIV : International Journal on Informatics Visualization
ISSN 2549-9610  (print) | 2549-9904 (online)
Organized by Department of Information Technology - Politeknik Negeri Padang, and Institute of Visual Informatics - UKM and Soft Computing and Data Mining Centre - UTHM
W :
E :,,

View JOIV Stats

Creative Commons License is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.