Computer Science and Information Technologies
Vol 4, No 1: March 2023

Comparative study of ensemble deep learning models to determine the classification of turtle species

Ruvita Faurina (University of Bengkulu)
Andang Wijanarko (University of Bengkulu)
Aknia Faza Heryuanti (University of Bengkulu)
Sahrial Ihsani Ishak (IPB University)
Indra Agustian (University of Bengkulu)



Article Info

Publish Date
01 Mar 2023

Abstract

Sea turtles are reptiles listed on the international union for conservation of nature (IUCN) red list of threatened species and the convention on international trade in endangered species of wild fauna and flora (CITES) Appendix I as species threatened with extinction. Sea turtles are nearly extinct due to natural predators and people who are frequently incorrect or even ignorant in determining which turtles should not be caught. The aim of this study was to develop a classification system to help classify sea turtle species. Therefore, the ensemble deep learning of convolutional neural network (CNN) method based on transfer learning is proposed for the classification of turtle species found in coastal communities. In this case, there are five well-known CNN models (VGG-16, ResNet-50, ResNet-152, Inception-V3, and DenseNet201). Among the five different models, the three most successful were selected for the ensemble method. The final result is obtained by combining the predictions of the CNN model with the ensemble method during the test. The evaluation result shows that the VGG16 - DenseNet201 ensemble is the best ensemble model, with accuracy, precision, recall, and F1-Score values of 0.74, 0.75, 0.74, and 0.76, respectively. This result also shows that this ensemble model outperforms the original model.

Copyrights © 2023






Journal Info

Abbrev

csit

Publisher

Subject

Computer Science & IT Engineering

Description

Computer Science and Information Technologies ISSN 2722-323X, e-ISSN 2722-3221 is an open access, peer-reviewed international journal that publish original research article, review papers, short communications that will have an immediate impact on the ongoing research in all areas of Computer ...