KLASIFIKASI SPESIES BURUNG MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

EDRIAL, MUHAMMAD RIFQI and Utami, Alvi Syahrini (2025) KLASIFIKASI SPESIES BURUNG MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK. Undergraduate thesis, Sriwijaya University.

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Abstract

Indonesia, with its high biodiversity, is home to 1,539 bird species, some of which have morphological similarities that make manual identification challenging.This research aims to develop a bird species classification model in Indonesia using a Convolutional Neural Network (CNN) with the EfficientNet-B0 architecture to improve identification accuracy and speed. The model was trained using 10 Indonesian bird species taken from 500 bird species classification secondary image dataset on Kaggle. This study compares the performance of the Rectified Linear Unit (ReLU) and Swish activation functions to determine their impact on the model's performance. The results indicate that the CNN model with the EfficientNet-B0 architecture and the Rectified Linear Unit activation function achieved the highest accuracy of 99.7%, with a precision of 99.8%, recall of 99.7%, and an f1-score of 99.7%. This performance demonstrates that the developed model can be an effective solution for automated and accurate bird species classification.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi, Convolutional Neural Network, Burung
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Muhammad Rifqi Edrial
Date Deposited:	17 Oct 2025 07:45
Last Modified:	17 Oct 2025 07:45
URI:	http://repository.unsri.ac.id/id/eprint/185237

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