Perbandingan Segmentasi Pada Citra Asli Dan Citra Kompresi Wavelet Untuk Identifikasi Telur

Saifullah, Shoffan; Sunardi, Sunardi; Yudhana, Anton

doi:10.33096/ilkom.v8i3.75.190-196

Cited by 11 publications

(7 citation statements)

References 3 publications

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“…Segmentation used the results of image quality improvement. This process only used the conversion of a grayscale image into a BW image [13], [39] by changing the value of a grayscale image (range 0-255) into a BW image with a limited median value. Matrix values above 128 are changed to 255, and below 128 are converted to 0.…”

Section: Methodsmentioning

confidence: 99%

Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods

Saifullah

Suryotomo

Yuhefizar

2021

RESTI

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This study aims to identify chicken egg embryos with the concept of image processing. This concept uses input and output in images. Thus the identification process, which was originally carried out using manual observation, was developed by computerization. Digital images are applied in identification by various image preprocessing, image segmentation, and edge detection methods. Based on these three methods, image processing has three processes: image grayscaling (convert to a grayscale image), image adjustment, and image enhancement. Image adjustment aims to clarify the image based on color correction. Meanwhile, image enhancement improves image quality, using histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization methods (CLAHE). Specifically for the image enhancement method, the CLAHE-HE combination is used for the improvement process. At the end of the process, the method used is edge detection. In this method, there is a comparison of various edge detection operators such as Roberts, Prewitt, Sobel, and canny. The results of edge detection using these four methods have the SSIM value respectively 0.9403; 0.9392; 0.9394; 0.9402. These results indicate that the SSIM values of the four operators have the same or nearly the same value. Thus, the edge detection method can provide good edge detection results and be implemented because the SSIM value is close to 1.00 (more than 0.93). Image segmentation detected object (egg and embryo), and the continued process by edge detection showed clearly edge of egg and embryo.

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Section: Methodsmentioning

confidence: 99%

Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods

Saifullah

Suryotomo

Yuhefizar

2021

RESTI

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“…Each image pixel is represented by 24 bits, namely 8 bits R (red), 8 bits G (green), 8 bits B (blue). The grayscale color space [15] is displayed in white with the highest intensity (255) [16] and black with the lowest power (0)-formulas in equations such as formula 4.…”

Section: Figure 5 Rgb Color Spacementioning

confidence: 99%

Identification of White Blood Cells Using Machine Learning Classification Based on Feature Extraction

Musliman

Fadlil

Yudhana

2021

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In various disease diagnoses, one of the parameters is white blood cells, consisting of eosinophils, basophils, neutrophils, lymphocytes, and monocytes. Manual identification takes a long time and tends to be subjective depending on the staff's experience, so the automatic identification of white blood cells will be faster and more accurate. White blood cells are identified by examining a colored blood smear (SADT) and examined under a digital microscope to obtain a cell image. Image identification of white blood cells is determined through HSV color space segmentation (Hue, Saturation Value) and feature extraction of the Gray Level Cooccurrence Matrix (GLCM) method using the Angular Second Moment (ASM), Contrast, Entropy, and Inverse Different Moment (IDM) features. The purpose of this study was to identify white blood cells by comparing the classification accuracy of the K-nearest neighbor (KNN), Naïve Bayes Classification (NBC), and Multilayer Perceptron (MLP) methods. The classification results of 100 training data and 50 white blood cell image testing data. Tests on the KNN, NBC, and MLP methods yielded an accuracy of 82%, 80%, and 94%, respectively. Therefore, MLP was chosen as the best classification model in the identification of white blood cells.

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“…Hasil akurasi yang diperoleh dengan menggunakan segmentasi citra adalah 82.3% dengan nilai k sebesar 8 [8]. Untuk proses segmentasi citra telur ayam juga telah dilakukan perbandingan pada citra asli dan citra yang dikompres menggunakan wavelet dan diperoleh hasil bahwa tidak ada perbedaan dalam mengidentifikasi telur ayam [9]. Fitur tekstur citra telur ayam juga digunakan untuk klasifikasi mutu citra telur ayam dengan menggunakan statistik orde pertama.…”

Section: Pendahuluanunclassified

Model Machine Learning untuk Klasifikasi Mutu Telur Ayam Ras Berdasarkan Kebersihan Kerabang

Maimunah

Primadewi²

2021

Jur. Ris. Kom.

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Eggs are one of the food needs that have high enough nutrition and are the main needs for the community. Therefore, chicken eggs consumed by the community must have good quality so that they are safe and useful when consumed by the community. According to SNI 3926:2008, the quality of eggs is divided into 3, namely quality I, quality II and quality III in terms of external and internal conditions. In this study, a classification was carried out to determine the quality of chicken eggs based on the cleanliness of the shell using a machine learning approach. Several methods in machine learning such as KNN, Naïve Bayes, Decision Tree and SVM are used to classify chicken egg quality based on shell cleanliness. The stages in this research include data acquisition, preprocessing, feature extraction, modeling and model evaluation. A total of 90 chicken egg image data were used in this study with a total of 30 images in each of each quality. In the preprocessing stage, filtering and conversion of images to grayscle images is carried out. To get the texture features of the chicken egg image, feature extraction is carried out using first and second order statistical calculations. The results of feature extraction obtained mean, skewness variance, kurtosis, entropy, Angular Second Moment (ASM), contrast, correlation variance, inverse different moment, entropy. These feature values are then partitioned into training data and test data. A total of 3 experiments were carried out to perform classification, namely the composition of the comparison of training data and test data. Three experiments were carried out by varying the ratio of the amount of training data and test data as much as 70%: 30%; 75%:25% and 80%:20%. The next stage is classification using several methods in machine learning, namely KNN, Naive Bayes, Decision Tree and SVM. The method that produces the best accuracy is KNN and Decision Tree with an accuracy of 96% with a comparison composition of training data and test data of 70%: 30% and 75%: 25%.

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Perbandingan Segmentasi Pada Citra Asli Dan Citra Kompresi Wavelet Untuk Identifikasi Telur

Cited by 11 publications

References 3 publications

Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods

Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods

Identification of White Blood Cells Using Machine Learning Classification Based on Feature Extraction

Model Machine Learning untuk Klasifikasi Mutu Telur Ayam Ras Berdasarkan Kebersihan Kerabang

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