Perbandingan Convolution Neural Network Untuk Klasifikasi Kesegaran Ikan Bandeng Pada Citra Mata

Prasetyo, Eko and Purbaningtyas, Rani and Adityo, Raden Dimas and Prabowo, Enrico Tegar and Ferdiansyah, Achmad Irfan (2021) Perbandingan Convolution Neural Network Untuk Klasifikasi Kesegaran Ikan Bandeng Pada Citra Mata. Jurnal Teknologi Informasi dan Ilmu Komputer, 8 (3). pp. 601-607. ISSN 2355-7699

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Abstract

Fish, one source of animal protein, is an exciting food for Indonesia's people. From a survey of food-ingredients demanded, milkfish are ranked fourth compared to other food-ingredients. Especially for milkfish, this fish is one of the six fish consumed by Indonesia's people besides tuna, bloating, anchovies, tilapia, and catfish, so the exactitude of the people when buying is a severe concern in choosing fresh milkfish. Detection of freshness by touching the fish's body may cause unexpected destruction, so detecting the fish's freshness should be conducted without touching using the eye image. In this research, we conducted an experimental implementation of freshness milkfish classification (vastly fresh and not fresh) based on the eyes using transfer learning from several CNNs, such as Xception, MobileNet V1, Resnet50, and VGG16. The experimental results of the classification of two milkfish freshness classes using 154 images show that VGG16 achieves the best performance compared to other architectures, where the classification accuracy achieves 0.97. With higher accuracy than other architectures, VGG16 is relatively more appropriate for classifying two classes of milkfish freshness.

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