PENGELOMPOKAN KESEGARAN IKAN MELALUI CITRA MATA IKAN MENGGUNAKAN METODE CNN (Convolution Neural Network)

  • Roby Saputra Universitas Maritim Raja Ali Haji
  • Tekad Matulatan Universitas Maritim Raja Ali Haji
  • Nurul Hayaty Universitas Maritim Raja Ali Haji
Keywords: CNN, Convolution Neural Network, kesegaran ikan, Scolopsis Monogramma (Monogrammed Monocle Bream), citra mata ikan

Abstract

The purpose of this study was to examine the Convolution Neural Network method for applying freshness levels of fish using fish eye images. Convolutional Neural Network (CNN) is a new model in the area of object recognition. Specifically for spatial data type input, CNN has a special layer, the convolution layer and the pooling layer that allows the hierarchical learning process of data features. The amount of data used for this study is 525 training data, 77 testing data, and 18 of test data with 9 categories, namely fish alive, dead on the first day, frozen 2 days, frozen 3 days, frozen 4 days, frozen 5 day, freeze 6 days, freeze 7 days, and rot. From 18 test data used there were 72,15% photos of fish eyes in accordance with the classification carried out and the highest epoch results displayed reached 98,44% accuracy.

Published
2020-05-11
Section
Teknik Informatika