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All Journal Journal of Engineering, Technology, and Applied Science (JETAS)
Muhaiyuddin, Arina Syakirah
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Physics

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Bird Species Classification System Using Transfer Learning Saad, Ahmad Fadli; Muhaiyuddin, Arina Syakirah; Iksan, Nur
Journal of Engineering, Technology, and Applied Science (JETAS) Vol 6 No 2: August 2024
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.jetas-0602.651

Abstract

Manual recognition is limited by the observer's expertise and knowledge, which can lead to errors when observed by non-experts. The objective of this research is to create a machine learning algorithm that can accurately classify bird species based on physical features and develop a software system that includes the machine learning algorithm, allowing for efficient classification of different bird species. This research also wants to evaluate the accuracy of the approach in real-world scenarios. The methodology research uses the machine learning life cycle model and software development life cycle model. The research aims to provide a user-friendly interface that recommends bird species classifications based on uploaded images, ultimately contributing to a more accessible and informative bird identification experience. In this research, the F1-score with fine-tuning is reported as 0.8889. It is close to 1, it suggests a well-balanced performance in terms of correctly identifying positive instances which is precision, and capturing relevant positive instances which are recalled. Based on the result, the proposed system can enhance users' ability to accurately identify and classify various bird species through the utilization of a pre-trained convolutional neural network model.
Co-Authors Nur Iksan Saad, Ahmad Fadli