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

17
17
Reset filters

Settings
Now showing 1 - 10 of 17
  • Publication
    An Intelligent Classification System for Trophozoite Stages in Malaria Species
    ( 2022-01-01)
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Mohd Yusoff Mashor
    ;
    Mohamed Z.
    ;
    Way Y.C.
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Jusman Y.
    Malaria is categorised as a dangerous disease that can cause fatal in many countries. Therefore, early detection of malaria is essential to get rapid treatment. The malaria detection process is usually carried out with a 100x magnificat i on of t hi n bl ood smear usi ng mi croscope observat i on. However, t he microbiologist required a long time to identify malaria types before applying any proper treatment to the patient. It also has difficulty to differentiate the species in trophozoite stages because of similar characteristics between species. To overcome these problems, a computer-aided diagnosis system is proposed to classify trophozoite stages of Plasmodium Knowlesi (PK), Plasmodium Falciparum (PF) and Plasmodium Vivax (PV) as early species identification. The process begins with image acquisition, image processing and classification. The image processing involved contrast enhancement using histogram equalisation (HE), segmentation procedure using a combination of hue, saturation and value (HSV) color model, Otsu method and range of each red, green and blue (RGB) color selections, and feature extraction. The features consist of the size of infected red blood cell (RBC), brown pigment in the parasite, and texture using Gray Level Co-occurrence Matrix (GLCM) parts. Finally, the classification method using Multilayer Perceptron (MLP) trained by Bayesian Rules (BR) show the highest accuracy of 98.95%, rather than Levenberg Marquardt (LM) and Conjugate Gradient Backpropagation (CGP) training algorithms.
  • Publication
    Classification System of Malaria Disease with Hu Moment Invariant and Support Vector Machines
    ( 2022-01-01)
    Jusman Y.
    ;
    Pikriansah
    ;
    Ardiyanto Y.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Mohamed Z.
    ;
    Hassan R.
    Malaria is an infectious disease caused by a plasmodium parasite transmitted by the female Anopheles mosquito. According to the World Health Organization (WHO) in 2020 there are an estimated 241 million cases of malaria worldwide with an estimated global death stood at 627. 000. The standard method of malaria diagnosis is by conducting microscopic examination or laboratory test and Rapid Diagnostic Test (RDT). Laboratory tests have a high risk of human error whereas RDT has weaknesses in temperature sensitivity, genetic variation, and antigen resistance in the bloodstream. This research offers a classification system of malaria disease by applying the Hu moment invariant and Support vector Machine (SVM) method with 3 types of malaria parasitic objects, namely falciparum, Malaria and vivax. The classification system uses 3 SVM models, namely linear SVM, polynomial SVM and Gaussian SVM with the Falciparum class as a positive data and malaria and vivax as negative data. The best classification outcome is on the Gaussian SVM model with 96.67% sensitivity and 90% specificity. The mean accuracy of the Gaussian SVM model with a 5-fold cross Validation 90 image sample which is divided into 3 classes is 86.66%.
  • Publication
    Features Extraction to Differentiate of Spinal Curvature Types using Hue Moment Algorithm
    ( 2020-03-10)
    Salleh M.A.M.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Jusman Y.
    ;
    Yusof M.I.
    Nowadays, diagnosing the spinal problems is very important to medical field. The objective of this research is to develop feature extraction technique to obtain the features, which automatically differentiate images of normal and abnormal (scoliosis) spinal curvatures. The process to extract features of spinal image start with image acquisition, image processing (i.e. enhancement, filtering, and segmentation). For image processing method, the most important part in this phase is the segmentation using manual threshold method. After the segmentation, hue moment for size and parameter are used to extract features that should be considered based on probabilistic to classify the spine images. The final experimental result shows that the developed features extraction technique can differentiate between normal and scoliosis spine images.
  • Publication
    Automatic Recognition System of Iron Deficiency Anaemia in Human RBC using Machine Learning Techniques
    ( 2023-01-01)
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Jusman Y.
    ;
    Shazmin Aniza Abdul Shukor
    ;
    Ibrahim W.N.A.B.W.
    ;
    Nordin S.A.
    ;
    Tohit E.R.B.M.
    ;
    Ali H.B.
    ;
    Nordiana Shariffudin
    ;
    Siti Marhainis Othman
    Iron Deficiency Anaemia (IDA) is the most common blood disorder. According to WHO, 30% of women aged 15-49 years, 37% of pregnant women, as well as 40% of children aged 6-59 months are anaemic globally. Anaemia can cause premature birth and affect mental, physical, and cognitive development, which in turn will lead to birth weight problems and stunted birth. The process of detecting IDA is usually captured based on a thin blood smear utilizing microscopic observation. Nevertheless, this process can be time-consuming. Moreover, it is challenging to identify the difference between IDA and normal red blood cells (RBCs) because the size is similar based on the observation of the human eye. It will cause difficulty in giving drug treatment to patients. A computeraided diagnosis (CAD) method was created to automatically distinguish between IDA and normal RBCs. The processes started with image acquisition, image processing, and recognition. Additionally, a Graphical User Interface (GUI) is also used to display images. In conclusion, recognition was done using the Multilayer Perceptron (MLP) method. The findings indicate that the proposed automated system is effective at distinguishing between IDA as well as normal RBCs, having an accuracy of 97.58% with regard to training and 98% regarding validation utilizing Levenberg-Marquardt (LM) trained MLP.
  • Publication
    Comparison of Multi Layered Percepton and Radial Basis Function Classification Performance of Lung Cancer Data
    ( 2020-03-10)
    Jusman Y.
    ;
    Indra Z.
    ;
    Salambue R.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Nurkholid M.A.F.
    Lung cancer was the most commonly diagnosed cancer as well as the leading cause of cancer death in males in 2008 globally. The way used to detect lung cancer are through examination chest X-ray, Computed Tomography (CT) scan, and Magnetic Resonance Imaging results. The accurate and efisien analysis of the imaging results are important to ensure the minimal time processing. A computed assisted diagnosis system is the crusial research which can conduct the analysis efficiently and efectively. This paper aimed to compare the classification performances of Multi Layered Perceptron (MLP) and Radial Basis Function (RBF) techniques. The public lung cancer datasets was used as training and testing data in the classfication techniques. Ten fold cross validation was used for dividing data before classifying techniques. The accuracy performances are compared to check a better technique for classification step.
  • 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
    An Improved Performance of Chest Physiotherapy using Vibration
    ( 2023-01-01)
    Loniza E.
    ;
    Setiawan R.A.
    ;
    Jusman Y.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Chairunnisa K.
    Data storage capabilities is a major problem which must be resolved in a chest physiotherapy device using vibration method to give effectiveness to therapist. This study aims to design a vibration-based Chest Physiotherapy tool with data storage capabilities, specifically tailored to address the clearance of secretions or phlegm in bronchitis patients. The research methodology employs frequency testing, input voltage measurement, and timer testing to compare the performance of the designed tool against a reference tool set consisting of a tachometer, Avometer, and stopwatch. The selection of the vibration method as the primary focus is justified by its comprehensive nature, encompassing other relevant methodologies. A notable innovation in this study is the inclusion of a memory card data storage mode, enabling real-time control and assessment of therapist effectiveness during therapy sessions. By leveraging the vibration-based chest physiotherapy method with enhanced data storage features, optimal airflow can be restored, overcoming blockages caused by the presence of secretions in the external respiratory tract.
  • 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.
  • Publication
    Comparison between Support Vector Machine and K-Nearest Neighbor Algorithms for Leukemia Images Classification Using Shape Features
    ( 2021-01-01)
    Jusman Y.
    ;
    Hasanah A.N.
    ;
    Purwanto K.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Riyadi S.
    ;
    Hassan R.
    ;
    Mohamed Z.
    Leukemia occurs when the body produces abnormal white blood cells in amounts exceeding the normal limit, making them misfunctioning. It is highly influential on the human immune system. Currently, medical personnel require a long time to recognize leukemia, and it is difficult to distinguish between acute leukemia cells and normal cells. Hence, this study aims to build a system program using white blood cell images with image processing using feature extraction with the Hu moments invariant and the Support Machine Machine (SVM) and K-Nearest Neighbor (K-NN) classification methods. The samples used were 800 blood images divided into two classes, acute and normal, with each class consisting of 400 sample images. Based on the test results from comparing the average value of accuracy and training time in both methods, the highest accuracy value was in the SVM method, with an accuracy of 87.97% and the K-NN method of 83.96%. The fastest training time was in the K-NN method of 2.43 seconds and the SVM method of 3.73 seconds.
  • Publication
    Comparison of Malaria Parasite Image Segmentation Algorithm Using Thresholding and Watershed Method
    ( 2021-02-12)
    Jusman Y.
    ;
    Pusparini A.
    ;
    Nazilah Chamim A.N.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    Malaria is an infectious disease caused by plasmodium that lives and breeds in the red blood cells, transmitted by the Anopheles mosquito. During this time, the paramedics to diagnose symptoms use any imagery that is done manually. In the identification analysis of the malaria parasite cell infection, there is a possibility of human error factor done by paramedics because of the number of samples analyzed. This case is because the human eye tends to be tired while working continuously, leading to misclassification and treatment that is not right. Therefore, it takes a computer-based system that facilitates image processing to paramedics or laboratory technicians to identify the parasite cells and reduce human error instances. This research conducted on identification of the thresholding and watershed of segmentation method for three types of plasmodium parasite, namely Plasmodium falciparum, Plasmodium malaria, and Plasmodium vivax. This study offered modifications thresholding and watershed algorithm. The results showed the success of the technique that can effectively segment on the three types of Plasmodium malaria, which has an accuracy rate above 90% as well as the results of the computation time between the thresholding method could segment imagery for 1-2 seconds and the watershed method intelligent segmented representation for 3-4 seconds.