Siti Nurul Aqmariah Mohd Kanafiah
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Preferred name
Siti Nurul Aqmariah Mohd Kanafiah
Official Name
Siti Nurul Aqmariah, Mohd Kanafiah
Alternative Name
Kanafiah, S. N.Aqmariah
Kanafiah, S. N.A.M.
Kanafiah, Siti Nurul Aqmariah Mohd
Aqmariah Kanafiah, S. N.
Kanafiah, S. N. A. M
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Scopus Author ID
55987982900
Researcher ID
HTR-1815-2023

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Analysis of Features Extraction Performance to Differentiate of Dental Caries Types Using Gray Level Co-occurrence Matrix Algorithm

2020-08-01 , Jusman Y. , Tamarena R.I. , Puspita S. , Saleh E. , Siti Nurul Aqmariah Mohd Kanafiah

This study analyzes the features extraction performance of dental caries image using Gray Level Cooccurrence Matrix (GLCM) algorithm for contrasted two types of caries is based on the theory of GV Black, namely: Dental caries Class 3 and Class 4. The study aims to determine the pixel value and quantization value of the GLCM used for an automated classification system of dental caries types. The analysis is conducted by using variations of pixel distances and quantization value to perform features on the image in values such as contrast, correlation, energy, and homogeneity. Then these values are used as input to the classification stage Knearest neighbor (KNN). Result performed on four data sets containing 60 images of each set is an accuracy value. The highest performance obtained is 80% of accuracy in 100 and 200 of pixel distances and 16 and 32 of quantization value. The pixel distances and quantization values are recommended to be used for an automated classification system of dental caries types based on X-ray images.

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Application of Watershed Algorithm and Gray Level Co-Occurrence Matrix in Leukemia Cells Images

2020-06-01 , Jusman Y. , Dewiprabamukti L.A. , Chamim A.N.N. , Mohamed Z. , Siti Nurul Aqmariah Mohd Kanafiah , Halim N.H.A.

A long with the development of technology, the image of a sample of leukemia can be digitally processed to reduce the level of human error in diagnosing the disease. This research conducted by designing the image processing system on two types of leukemia, there are Acute Myelogenous Leukemia (AML) and Normal cell images by applying Watershed segmentation methods and feature extraction Gray Level Co-Occurrence Matrix (GLCM). The system was designed to find out how effective these methods to be continue into the classification process. The results of testing the application of these methods are the accuracy of the watershed segmentation method for this kind of Normal class was 90.4% with the average computing time of 0.89 seconds, and for the class of AML is 100% with the average computation time of 0.94 seconds. Application of the method GLCM has a significant difference the two types of leukemia were examine for each value extraction features with faster computing time, average 0.0060 seconds of computing time for this kind of Normal images. Whereas, for the AML class with average computation time was 0.0054 seconds.

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