Improvement of Starling Image Classification with Gabor and Wavelet Based on Artificial Neural Network

Aviv Rahman - Universitas Widyagama Malang, Indonesia
Istiadi Istiadi - Universitas Widyagama Malang, Indonesia
April Hananto - Universitas Buana Perjuangan Karawang, Indonesia
Ahmad Fauzi - Universitas Buana Perjuangan Karawang, Indonesia

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Indonesia is a country that has a diversity of animal species with the top 10 predicate in the world. The population of animal species, including starlings, is very widely known in the country. Starlings currently in Indonesia are diverse, ranging from standard to rare in Indonesia. This starling has its characteristics based on the type, color, sound, etc. In the first problem, the first accuracy performance when using the GLCM texture feature with Artificial Neural Network is 68%. Furthermore, the second problem is the accuracy performance of typing using the GLCM texture feature with a Decision Tree of 50%. This research aims to improve the starling classification system accuracy using Gabor and Wavelet texture features with artificial Neural Networks. Based on testing in the classification of starlings using the GLCM, Gabor, and Wavelet features, the highest degree of precision can, therefore, be concluded to be at the GLCM and Wavelet feature levels. The GLCM and Wavelet level accuracy results reached 83% at a rate of learning 0.9. In the experiments that have been done, the GLCM and Wavelet levels can increase accuracy using Artificial Neural Networks. In the classification process, the type of starlings also shows that the computational time in testing is much faster in producing accuracy values. In addition, the accurate accuracy while testing the starling category also increases.


Artificial Neural Network; Starling; GLCM; Gabor; Wavelet

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M. Jogin, Mohana, M. S. Madhulika, G. D. Divya, R. K. Meghana, and S. Apoorva, “Feature extraction using convolution neural networks (CNN) and deep learning,” 2018 3rd IEEE Int. Conf. Recent Trends Electron. Inf. Commun. Technol. RTEICT 2018 - Proc., no. November, pp. 2319–2323, 2018, doi: 10.1109/RTEICT42901.2018.9012507.

F. Rachmawati and D. Widhyaestoeti, “Early Warning System for Predicting the Level of Road Service on the Bogor City SSA Route,” vol. 8, no. 2, pp. 9–18, 2020.

B. S. Iskandar, D. Mulyanto, and R. L. Alfian, “Traditional Ecological Knowledge Of The Bird Traders On Bird Species Bird Naming , And Bird Market Chain : A Case Study In Bird Market Pasty Yogyakarta , Indonesia,” vol. 21, no. 6, pp. 2586–2602, 2020, doi:

I. C. Navotas, C. N. V. Santos, E. J. M. Balderrama, F. E. B. Candido, A. J. E. Villacanas, and J. S. Velasco, “Fish Identification And Freshness Classification Through Image Processing Using Artificial Neural Network,” ARPN J. Eng. Appl. Sci., vol. 13, no. 18, pp. 4912–4922, 2020.

A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, “A survey of the recent architectures of deep convolutional neural networks,” Artif. Intell. Rev., vol. 53, no. 8, pp. 5455–5516, 2020, doi: 10.1007/s10462-020-09825-6.

O. Meisner and J. Hyman, “Deep Convolutional Network For Animal Sound Classification And Source Attribution Using Dual Audio Recordings,” vol. 145, no. 2, pp. 654–662, 2019, doi:

Q. Zhang, Y. N. Wu, and S. C. Zhu, “Interpretable Convolutional Neural Networks,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 8827–8836, 2018, doi: 10.1109/CVPR.2018.00920.

S. S. Dhakshaya and D. Jeraldin Auxillia, “Classification of ECG using convolutional neural network (CNN),” 2019 Int. Conf. Recent Adv. Energy-Efficient Comput. Commun. ICRAECC 2019, vol. 19, no. 2, 2019, doi: 10.1109/ICRAECC43874.2019.8995096.

L. A. Latumakulita et al., “Combination of Feature Extractions for Classification of Coral Reef Fish Types Using Backpropagation Neural Network,” Int. J. Informatics Vis., vol. 6, no. 3, pp. 643–649, 2022.

A. J. Khalil, A. M. Barhoom, B. S. Abu-Nasser, M. M. Musleh, and S. S. Abu-Naser, “Energy Efficiency Prediction Using Artificial Neural Network,” Int. J. Acad. Pedagog. Res., vol. 3, no. 9, pp. 1–7, 2019.

V. M. Mulia Siregar and H. Sugara, “Implementation Of Artificial Neural Network To Assesment The Lecturer’s Performance,” IOP Conf. Ser. Mater. Sci. Eng., vol. 420, no. 1, 2019, doi: 10.1088/1757-899X/420/1/012112.

E. T. Lau, L. Sun, and Q. Yang, “Modelling, Prediction And Classification Of Student Academic Performance Using Artificial Neural Networks,” SN Appl. Sci., vol. 1, no. 9, pp. 1–10, 2019, doi: 10.1007/s42452-019-0884-7.

M. Al-Shawwa, A. Al-Rahman Al-Absi, S. Abu Hassanein, K. Abu Baraka, and S. S. Abu-Naser, “Predicting Temperature And Humidity In The Surrounding Environment Using Artificial Neural Network,” Int. J. Acad. Pedagog. Res., vol. 2, no. 9, pp. 1–6, 2020.

H. Yu, D. C. Samuels, Y. yong Zhao, and Y. Guo, “Architectures And Accuracy Of Artificial Neural Network For Disease Classification From Omics Data,” BMC Genomics, vol. 20, no. 1, pp. 1–12, 2019, doi: 10.1186/s12864-019-5546-z.

Y. Widhiyasana et al., “Genetic Algorithm for Artificial Neural Networks in Real-Time Strategy Games,” Int. J. Informatics Vis., vol. 6, no. June, pp. 298–305, 2022, doi: 10.30630/joiv.6.2.990.

Ş. Öztürk and B. Akdemir, “Application of Feature Extraction and Classification Methods for Histopathological Image using GLCM, LBP, LBGLCM, GLRLM and SFTA,” J. Comput. Sci., vol. 132, no. Iccids, pp. 40–46, 2020, doi: 10.1016/j.procs.2018.05.057.

X. Zhang, Y. Sun, and W. Qi, “Hyperspectral Image Classification Based on Extended Morphological Attribute Profiles and Abundance Information,” Work. Hyperspectral Image Signal Process. Evol. Remote Sens., vol. 2018-Septe, no. April, pp. 1–5, 2018, doi: 10.1109/WHISPERS.2018.8747090.

S. Kusumaningtyas and R. A. Asmara, “Color Using Artificial Neural Network Method,” CESS (Journal Comput. Eng. Syst. Sci., vol. 2, pp. 72–75, 2019, doi:

B. Yanuki, A. Y. Rahman, and Istiadi, “Image Classification of Canaries Using Artificial Neural Network,” 2021 5th Int. Conf. Informatics Comput. Sci., pp. 12–17, 2021, doi: 10.1109/icicos53627.2021.9651905.

A. L. Hananto, S. Sulaiman, and S. Widiyanto, “Comparison of vertical distance and sliding windows method in brass plated tire steel cord (bptsc) diameter measurement,” ICIC Express Lett., vol. 14, no. 11, pp. 1129–1138, 2020, doi: 10.24507/icicel.14.11.1129.

A. Y. Rahman, “Classification of Starling Image Using Artificial Neural Networks,” ACM Int. Conf. Proceeding Ser., pp. 309–314, 2021, doi: 10.1145/3479645.3479690.

I. M. Nasser and S. S. Abu-Naser, “Artificial Neural Network For Predicting Animals Category,” vol. 3, no. 2, pp. 18–24, 2019, doi: 10.256W1/123456789/182.

A. Y. Rahman, “Image Classification of Starlings Using Artificial Neural Network and Decision Tree,” in 2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), 2022, pp. 286–291, doi: 10.1109/icicos53627.2021.9651905.

Hariyanto, S. A. Sudiro, and S. Lukman, “Minutiae matching algorithm using artificial neural network for fingerprint recognition,” 3rd Int. Conf. Artif. Intell. Model. Simul., pp. 37–41, 2020, doi: 10.1109/AIMS.2015.16.

Z. Wang et al., “Fully Memristive Neural Networks For Pattern Classification With Unsupervised Learning,” Nat. Electron., vol. 1, no. 2, pp. 137–145, 2020, doi: 10.1038/s41928-018-0023-2.

H. D. Putranto, B. Brata, and Y. Yumiati, “Ex-Situ Population Of White-Rumped Shama (Copsychus Malabaricus): Studies Of Density, Distribution And Bird Keepers In Bengkulu, Sumatra,” vol. 21, no. 3, pp. 865–874, 2020, doi: 10.13057/biodiv/d210303.

M. Jha, “Smart Intelligent Computing And Applications,” ICT Express, vol. 104, p. 689, 2019, doi: 10.1007/978-981-13-1921-1.

B. S. Rem et al., “Identifying Quantum Phase Transitions Using Artificial Neural Networks On Experimental Data,” Nat. Phys., vol. 15, no. 9, pp. 917–920, 2019, doi: 10.1038/s41567-019-0554-0.

A. Lia Hananto et al., “Analysis of Drug Data Mining with Clustering Technique Using K-Means Algorithm,” J. Phys. Conf. Ser., vol. 1908, no. 1, 2021, doi: 10.1088/1742-6596/1908/1/012024.

A. Lia Hananto, B. Priyatna, A. Fauzi, A. Yuniar Rahman, Y. Pangestika, and Tukino, “Analysis of the Best Employee Selection Decision Support System Using Analytical Hierarchy Process (AHP),” J. Phys. Conf. Ser., vol. 1908, no. 1, 2021, doi: 10.1088/1742-6596/1908/1/012023.

P. Molchanov, R. I. A. Harmanny, J. J. M. De Wit, K. Egiazarian, and J. Astola, “Classification Of Small Uavs And Birds By Micro-Doppler Signatures,” Int. J. Microw. Wirel. Technol., vol. 6, no. 3–4, pp. 435–444, 2021, doi: 10.1017/S1759078714000282.

R. P. Tivarekar, V. D. Chavan, S. A. Shete, and A. B. Vartak, “Audio based Bird Species Recognition Using Naïve Bayes Algorithm,” Int. J. Mod. Trends Eng. Res., vol. 5, no. 1, pp. 117–124, 2020, doi: 10.21884/ijmter.2018.5020.shojv.

C. Fang, T. Zhang, H. Zheng, J. Huang, and K. Cuan, “Pose Estimation And Behavior Classification Of Broiler Chickens Based On Deep Neural Networks,” Comput. Electron. Agric., vol. 180, no. October, p. 105863, 2021, doi: 10.1016/j.compag.2020.105863.

A. S. Talita, O. S. Nataza, and Z. Rustam, “Naïve Bayes Classifier and Particle Swarm Optimization Feature Selection Method for Classifying Intrusion Detection System Dataset,” J. Phys. Conf. Ser., vol. 1752, no. 1, 2021, doi: 10.1088/1742-6596/1752/1/012021.

S. R. Gomes et al., “A Comparative Approach To Email Classification Using Naive Bayes Classifier And Hidden Markov Model,” 4th Int. Conf. Adv. Electr. Eng. ICAEE 2019, vol. 2018-Janua, pp. 482–487, 2019, doi: 10.1109/ICAEE.2017.8255404.

S. Islam, S. I. A. Khan, M. Minhazul Abedin, K. M. Habibullah, and A. K. Das, “Bird Species Classification From An Image Using VGG-16 Network,” ACM Int. Conf. Proceeding Ser., pp. 38–42, 2019, doi: 10.1145/3348445.3348480.

F. I. Adiba, T. Islam, and M. S. Kaiser, “Effect Of Corpora On Classification Of Fake News Using Naive Bayes Classifier,” Int J Auto AI Mach Learn, vol. 1, no. 1, p. 80, 2020, doi: 10.1145/25637568.

J. Drdsh, D. Eleyan, and A. Eleyan, “A Prediction Olive Diseases Using Machine Learning Models, Decision Tree and Naïve Bayes Models,” J. Theor. Appl. Inf. Technol., vol. 99, no. 18, pp. 4231–4240, 2021, doi: 10.1007/18173195.

H. Njah, S. Jamoussi, and W. Mahdi, “Semi-Hierarchical Naïve Bayes Classifier,” Int. J. Microw. Wirel. Technol., no. iii, pp. 1772–1779, 2020, doi: 10.1088/9781509006205.

G. A. A. J. Alkubaisi, “The Role Of Ensemble Learning In Stock Market Classification Model Accuracy Enhancement Based On Naive Bayes Classifiers,” Int. J. Stat. Probab., vol. 9, no. 1, p. 36, 2019, doi: 10.5539/ijsp.v9n1p36.