Classification of Typical Food from Sulawesi using Artificial Neural Network and Wavelet Haar

Trisno, Salahudin Robo

2019

Abstract

Food is the important thing in every region, but not everyone knows the form and type of the food. Therefore, this article used backpropagation algorithm and wavelet haar 2D method to classify food. The research used appropriate image data and tested the images using 32*32*3 wavelet Haar 2 D which was changed to 3072. Extracted feature was processed into 1 dimensional and trained backpropagation neural network to be able to classify food. The result of backproppagation training was a dataset of 4.160 images. Samples with 10 iterations had 80 % training Acc and 80% validation Acc. Samples with 50 iterations had 81.63 % training Acc and 81.42% validation Acc. Samples with 100 iterations had 82.7 % training Acc and 82.71% validation Acc. Samples with 150 iterations had 83.29 % training Acc and 82.11% validation Acc and sample with 200 iterations had 84.31 % training Acc and 82.34% validation Acc.

Paper Citation

in Harvard Style

Trisno. and Robo S. (2019). Classification of Typical Food from Sulawesi using Artificial Neural Network and Wavelet Haar.In Proceedings of the International Conferences on Information System and Technology - Volume 1: CONRIST, ISBN 978-989-758-453-4, pages 330-335. DOI: 10.5220/0009909903300335

in Bibtex Style

@conference{conrist19,
author={Trisno and Salahudin Robo},
title={Classification of Typical Food from Sulawesi using Artificial Neural Network and Wavelet Haar},
booktitle={Proceedings of the International Conferences on Information System and Technology - Volume 1: CONRIST,},
year={2019},
pages={330-335},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009909903300335},
isbn={978-989-758-453-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the International Conferences on Information System and Technology - Volume 1: CONRIST,
TI - Classification of Typical Food from Sulawesi using Artificial Neural Network and Wavelet Haar
SN - 978-989-758-453-4
AU - Trisno.
AU - Robo S.
PY - 2019
SP - 330
EP - 335
DO - 10.5220/0009909903300335