Siti Nurul Aqmariah Mohd Kanafiah
Thumbnail Image
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

Research Output

Filters

2 1
2
2
Reset filters

Settings
Now showing 1 - 2 of 2
  • 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.
      2  9
  • Publication
    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.
      2  24