DOI: https://doi.org/10.14710/vol15iss2pp173-178

Identification of Grouper Fish Types using Convolutional Neural Network Resnet-50 Algorithm

*Rini Nuraini  -  FTKI, Universitas Nasional, Jl. Sawo Manila No.61, Pejaten, Jakarta, Indonesia 12510 , Indonesia
Wahyul Amien Syafei  -  Electrical Engineering, Faculty of Engineering, Diponegoro University, Jl. Prof. H. Soedarto, S.H., Tembalang, Semarang, Indonesia 50275, Indonesia
Adi Wibowo  -  Informatics Department, Faculty of Science and Mathematics, Diponegoro University, Jl. Prof. H. Soedarto, S.H., Tembalang, Semarang, Indonesia 50275, Indonesia
Indra Jaya  -  Faculty of Fisheries and Maritime Affairs, Institut Pertanian Bogor, Kampus IPB Dramaga, Jl. Agatis, Dramaga, Kec. Dramaga, Bogor, Indonesia 16680, Indonesia
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Abstract

Grouper is a type of fish that is popular with the public. It is necessary to identify the type of grouper fish based on color patterns with increase the epoch value to get the best accuracy. The purpose of the research is to predict the type of grouper. This research use CNN Resnet-50 algorithm. 30 data used. The accuracy of prediction is 75 % to predict the image groupers. In the grouper prediction process, the more we increase the epoch value, we will get the best accuracy value. Epoch is a factor that affects the time of training an AI model and affects the accuracy value of the AI model.

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Keywords: Grouper Fish; CNN Resnet-50; Epoch Value; Image Classification; Deep Learning

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Section: Research Articles
Language : EN
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