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
Main Affiliation
Scopus Author ID
55987982900
Researcher ID
HTR-1815-2023

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  • Publication
    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.
  • Publication
    Feature Extraction Performance to Differentiate Spinal Curvature Types using Gray Level Co-occurrence Matrix Algorithm
    ( 2020-11-24)
    Jusman Y.
    ;
    Lubis J.H.
    ;
    Chamim A.N.N.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    Spinal curvature type can be detected from digital X-ray images. Experts diagnose spinal curvature for a long time to obtain accurate results. This research aims to analyze the use of image processing techniques to extract features in two types of spinal imagery, normal and abnormal (i.e., scoliosis), by applying the Gray Level Co-occurrence Matrix (GLCM) algorithm and Support Vector Machine (SVM) for the classification method. This study used 40 images divided into 4 data sets for analysis. Three distance parameters, 50, 75, and 100 pixels, and three parameters of quantization values, 8, 16, and 32, were utilized for analysis. The highest accuracy obtained from one of the specific data set was 100%, while the highest accuracy of the average of each value distance and quantization was 90%. The GLCM algorithm could differentiate the abnormality of spinal imagery.