Content Based Image Retrieval Dengan Metode Color Moment Dan K-Means

Sukafona, I Made; Thalib, Emmy Febriani

doi:10.31598/jurnalresistor.v1i2.322

Cited by 2 publications

(1 citation statement)

References 1 publication

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“…: 1 represents H, 2 represents S, 3 represents V), j is Pixel order, Pij Defines the i-th value of the color component in the j-th of the image pixel, r represents Number of channels (e.g. 3) [13].…”

Section: Methodsmentioning

confidence: 99%

Image Extraction of Lettuce Leaves using Fast Fourier Transform Method and Color Moments

Erwanto

Utomo

2022

JEEMECS

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Lettuce (Lactuca sativa L.) is a seasonal leaf vegetable with high nutritional content which is usually fresh for consumption. Typical harvest age for lettuce is 45 days. Lettuce which is harvested more than 45 days will affect the taste of the lettuce. In addition to the lettuce’s age, there are several things that can affect taste of the lettuce, including room temperature, harvest time, and thickness of the leaf color. In this study, Fast Fourier transform (FFT) was used as feature extraction by changing the spatial domain in frequency domain image of the lettuce leaves, while the color moment method was used as the extraction of lettuce leaves. With this digital image processing, it can automatically identify maturity level of lettuce leaves. The classification process uses the Naïve Bayes method with the Weka application. The obtained results of texture and color extraction using FFT method and the color moment using Naïve Bayes classification method in the Weka application work well. From the results of the age classification of lettuce based on its leaf color, the average percentage of total accuracy was 94.4%. The correlation of color and taste using the correlation test which performed by SPSS, and it was found that there are positive relationship between color and taste with sig. (1-tailed) 0.05.

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Section: Methodsmentioning

confidence: 99%

Image Extraction of Lettuce Leaves using Fast Fourier Transform Method and Color Moments

Erwanto

Utomo

2022

JEEMECS

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Klasifikasi Pengenalan Buah Menggunakan Algoritma Naive Baiyes

Saputra

2019

jurnalresistor

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Manually sorting varieties of apples result in high costs, subjectivity, boredom, and inconsistencies associated with humans. A means is needed to distinguish between types of apples and, therefore, some reliable techniques are necessary to identify varieties quickly and without damage. The purpose of conducting research is to investigate the application and performance for Naive Bayes algorithm for apple varieties. This software methodology involves image acquisition, preprocessing, segmentation and analysis classification varieties for apple. The prototype of Apple's classification system was built using the MATLAB R2017 development platform environment. The results in this study indicate that the estimated average accuracy, sensitivity, precision, and specificity are 81%, 73%, 100%, and 70%, respectively. MLP-Neural shows that performance of the Naive Bayes technique is consistent with Principal, Fuzzy Logic, and Neural analysis with 89%, 91%, 87%, and 82% respectively in terms of accuracy. This study shows that Naif Bayes has excellent potential for identifying nondestructive and accurate apple varieties.

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Content Based Image Retrieval Dengan Metode Color Moment Dan K-Means

Cited by 2 publications

References 1 publication

Image Extraction of Lettuce Leaves using Fast Fourier Transform Method and Color Moments

Image Extraction of Lettuce Leaves using Fast Fourier Transform Method and Color Moments

Klasifikasi Pengenalan Buah Menggunakan Algoritma Naive Baiyes

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