Klasifikasi Citra Daging Menggunakan Deep Learning dengan Optimisasi Hard Voting

Kade Bramasta Vikana Putra; I Putu Agung Bayupati; Dewa Made Sri Arsa

doi:10.29207/resti.v5i4.3247

Kade Bramasta Vikana Putra Udayana University
I Putu Agung Bayupati Universitas Udayana
Dewa Made Sri Arsa Universitas Udayana

DOI: https://doi.org/10.29207/resti.v5i4.3247

Keywords: Deep learning, Ensemble Learning, Image Recognition, Transfer Learning

Abstract

Meat is a staple food for some Indonesian people, apart from the taste, meat also contains vitamins and minerals that are good for the human body, however, not all meat can be consumed by the Indonesian people. the texture and color of beef, pork and mutton have similarities and tend to be similar, therefore a system is needed to recognize the three types of meat. In this study, the authors use various types of Deep Learning architecture such as Resnet-50, VGG-16, VGG-19 and Densenet-121 with Hard Voting to improve the performance of Deep Learning in recognizing the three types of meat. The results show that Resnet-50 with Hard Voting can outperform Deep Learning Resnet-50, VGG-16, VGG-19 and Densenet-121- with f1 score 98.88%, precision 98.89% and recall 98.88%. in image classification of pork, beef and mutton.

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