Comparison of Classification of Birds Using Lightweight Deep Convolutional Neural Networks

  Aldi Jakaria (1*), Hilman Ferdinandus Pardede (2)

(1) Fakultas Teknologi Informasi, Universitas Nusa Mandiri - Indonesia
(2) Research Center for Data and Information Sciences, National Research and Innovation Agency - Indonesia orcid
(*) Corresponding Author

Received: September 21, 2022; Revised: November 23, 2022
Accepted: December 16, 2022; Published: December 31, 2022

How to cite (IEEE): A. Jakaria,  and H. F. Pardede, "Comparison of Classification of Birds Using Lightweight Deep Convolutional Neural Networks," Jurnal Elektronika dan Telekomunikasi, vol. 22, no. 2, pp. 87-94, Dec. 2022. doi: 10.55981/jet.503


Environmental scientists often use birds to understand ecosystems because they are sensitive to environmental changes, but few experts are available. To make it easier to recognize bird species, an automatic system that can classify bird species is needed. There are lots of models to choose from, but some models require very high computational data when training data, reducing training time can result in less wasted electrical energy so that it can have a good effect on the environment. For this reason, it is necessary to test a model that has a small complexity in training time, whether it can produce good performance. Based on the number of neural network models available, this study will classify using the EfficientNet, EfficientNetV2, MobileNet, MobileNetV2, and NasnetMobile models to determine whether these models can have good performance. From the research results, all the models tested have good performance with an accuracy between 95% - 97%. The MobileNetV2 model has the less efficiency with the smallest training time while maintaining good performance.



EfficientNet; EfficientNetV2; MobileNet; MobileNetV2; NasnetMobile

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