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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Akhmad Rohim
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Convolution Neural Network (CNN) Untuk Pengklasifikasian Citra Makanan Tradisional Akhmad Rohim; Yuita Arum Sari; Tibyani Tibyani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

People in this digital era take a picture before eating is one of lifestyle. Then the result of the picture will be uploaded to social media. Traditional food's pictures dissemination still less identified encourages this research about the classification of traditional food's image. Extraction of classification features food image is difficult because of food can vary dramatically in appearances such as shape, texture, color, and other visual properties. Convolution Neural Network (CNN) is a method that can learn its own features on a complex image. Hopefully, CNN evaluation results for the classification image of traditional food can provide a solution to identify the image of traditional food. Result of this research in building the architecture of the Convolutional Neural Network model for classification of the traditional food image required 4 conditional layers, 4 max-pooling layers, and 2 fully connected layers. That architecture obtained because it gets the smallest loss value with 0.000044 value on the 15 epoch during the learning process and gets a 73% precision, 69% recall, and 69% F-score.
Co-Authors Tibyani Tibyani Yuita Arum Sari