ANALISIS EFEKTIVITAS ALGORITMA EUCLIDEAN DISTANCE UNTUK DETEKSI WARNA DALAM FORMAT CIE LAB

Kemal Ardiansyah; Fathoni Mahardika; Deris Santika

doi:10.69933/jocsit.v1i2.50

Authors

Kemal Ardiansyah Universitas Sebelas April Sumedang Author
Fathoni Mahardika Universitas Sebelas April Sumedang Author
Deris Santika Universitas Sebelas April Sumedang Author

DOI:

https://doi.org/10.69933/jocsit.v1i2.50

Keywords:

euclidean distance, color detection, CIE Lab

Abstract

Color identification is the process of looking for a color that is defined by humans. This process is one of the basic parameters in other research which aims to identify fruit quality based on color. There are many methods that can be used to identify colors in digital images, including Euclidean Distance. The image entered into Euclidean Distance is LAB color space. Euclidean Distance will calculate the difference in distance from a point to a reference point. The reference point is obtained by finding the mode value of all orange image data. Previously, all image data had to be converted into pixels. Determining this reference point is greatly influenced by the total pixel size for each image. There are pixel data distribution factors that influence the reference point. For the orange color, the dataset was taken from the Kaggle website with the project name, color dataset for color recognition, with 25 images, an average accuracy of 96% was obtained.

Abstract

Color identification is the process of looking for a color that is defined by humans. This process is one of the basic parameters in other research which aims to identify fruit quality based on color. There are many methods that can be used to identify colors in digital images, including Euclidean Distance. The image entered into Euclidean Distance is LAB color space. Euclidean Distance will calculate the difference in distance from a point to a reference point. The reference point is obtained by finding the mode value of all orange image data. Previously, all image data had to be converted into pixels. Determining this reference point is greatly influenced by the total pixel size for each image. There are pixel data distribution factors that influence the reference point. For the orange color, the dataset was taken from the Kaggle website with the project name, color dataset for color recognition, with 25 images, an average accuracy of 96% was obtained.

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Author Biographies

Kemal Ardiansyah, Universitas Sebelas April Sumedang

Informatics Engineering College Student at Sebelas April University
Fathoni Mahardika, Universitas Sebelas April Sumedang

Lecturer in Informatics Engineering at Sebelas April University
Deris Santika, Universitas Sebelas April Sumedang

Lecturer in Informatics Engineering at Sebelas April University

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