The Optimization of CNN Algorithm Using Transfer Learning for Marine Fauna Classification

Authors

  • Insidini Fawwaz Universitas Prima Indonesia
  • Yennimar Universitas Prima Indonesia
  • N P Dharsinni Universitas Prima Indonesia
  • Bayu Angga Wijaya Universitas Prima Indonesia

DOI:

10.33395/sinkron.v8i4.12893

Keywords:

CNN; Transfer Learning; Deep Learning; Classification; Marine Fauna

Abstract

Marine fauna are all types of organisms that live in the marine environment. Marine fauna is also an important part of the marine ecosystem that has an important role in maintaining environmental balance. However, the survival of marine fauna is threatened due to activities carried out by humans, such as pollution, overfishing, industrial waste disposal into marine waters, plastic pollution and so on. Therefore, efforts are needed to monitor and protect marine fauna so that marine ecosystems can remain stable. One way to monitor marine fauna is by using classification technology. One of the technologies that can be used in marine fauna classification technology is Convolutional Neural Network (CNN).  CNN is one of the classification methods that can be used to classify objects in images with a high level of accuracy. The CNN architecture models used are MobileNet, Xception, and VGG19. Furthermore, the method used to improve the performance of the CNN algorithm is the Transfer Learning method. The test results show that the MobileNet architecture model produces the highest accuracy value of 91.94% compared to Xception and VGG19 which only get an accuracy value of 87.64% and 88.42%. This shows that the MobileNet model has a more optimal performance in classifying marine fauna.

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How to Cite

Fawwaz, I. ., Yennimar, Y., Dharsinni, N. P. ., & Wijaya, B. A. . (2023). The Optimization of CNN Algorithm Using Transfer Learning for Marine Fauna Classification. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(4), 2236-2245. https://doi.org/10.33395/sinkron.v8i4.12893

Issue

Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023

Section

Articles

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Copyright (c) 2023 Insidini Fawwaz, Yennimar, N P Dharsinni, Bayu Angga Wijaya

Creative Commons License

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

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