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Abdul Rachman Manga
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Analisis Perbandingan VGG-16 dan ResNet50 untuk Klasifikasi Multilabel Gambar Kerbau Toraja: Pendekatan Deep Learning Tri Anita Resky Ramadhani; Abdul Rachman Manga
Jurnal Teknik Vol 22 No 2 (2024): Jurnal Teknik
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37031/jt.v22i2.490

Abstract

This study aims to compare the performance of two Convolutional Neural Networks (CNN) models, namely VGG-16 and ResNet50, in the task of multilabel classification of buffalo images. The Dataset used consists of 2009 buffalo images labeled with five categories: human, motorcycle, truck, wild animal, and buffalo. The CNN models were trained using 30 epochs and evaluated using loss, accuracy, Precision, recall, and f1-score metrics. The experimental results show that VGG-16 consistently outperforms ResNet50 by achieving the highest accuracy of 0.95 in the training set and 0.94 in the validation set, and f1-score of 0.94 in the training set and 0.92 in the validation set. These findings indicate that a deeper and more structured CNN architecture, such as VGG-16, provides better results in classifying buffalo images with complex label variations.
Co-Authors Tri Anita Resky Ramadhani