Support Vector Machine for Classifying the Quality of the Egg based on the Color

Maimunah, Rahmadya Trias Handayanto, Taufiqur Rokhman

2018

Abstract

Egg quality is an indicator to determine the freshness of an egg. One of a method to determine the egg quality is through an analysis of eggshell condition. Color intensity of the eggshell comprises of three classes, i.e. dark-brown, brown, and light-brown colors. This classification is based on the pigment concentration and the structure of eggshell. Dark brown egg stronger and thicker than the light brown one. The quality of an egg decreases faster for bright eggshell’s color. This research classified the quality of broiler eggs based on the eggshell color using the support vector machine. This research used color red-green-blue (RGB) image of ninety eggs sample. Feature extraction was used to calculate the RGB of each egg. Normalization was used to get normalize RGB parameters (rgb) before classification through the use of support vector machine. Classification result showed the accuracy of 80 percent.

Paper Citation

in Harvard Style

Maimunah., Trias Handayanto R. and Rokhman T. (2018). Support Vector Machine for Classifying the Quality of the Egg based on the Color.In Proceedings of the 1st International Conference on Recent Innovations - Volume 1: ICRI, ISBN 978-989-758-458-9, pages 3092-3098. DOI: 10.5220/0009947630923098

in Bibtex Style

@conference{icri18,
author={Maimunah and Rahmadya Trias Handayanto and Taufiqur Rokhman},
title={Support Vector Machine for Classifying the Quality of the Egg based on the Color},
booktitle={Proceedings of the 1st International Conference on Recent Innovations - Volume 1: ICRI,},
year={2018},
pages={3092-3098},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009947630923098},
isbn={978-989-758-458-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Recent Innovations - Volume 1: ICRI,
TI - Support Vector Machine for Classifying the Quality of the Egg based on the Color
SN - 978-989-758-458-9
AU - Maimunah.
AU - Trias Handayanto R.
AU - Rokhman T.
PY - 2018
SP - 3092
EP - 3098
DO - 10.5220/0009947630923098