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

Research Output

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  • Publication
    Classification of Parasite Malaria in Schizon Stage with GoogleNet and VGG-19 Pre-Trained Models
    ( 2023-01-01)
    Jusman Y.
    ;
    Aftal A.A.
    ;
    Tyassari W.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Hayati N.
    ;
    Mohamed Z.
    The development of artificial intelligence technology has currently given benefit for humans in various fields. In the medical field, artificial intelligence was developed to help medical experts to classify various diseases using medical images, including malaria. Early detection of malaria parasites is important to save the patients, thus this study developed a detection system for some malaria parasites (P. falciparum, P. vivax, and P. malariae) in the schizont stage. This system uses deep learning methods using GoogleNet and VGG 19 pre-trained models. This study performs accuracy, running time, and analysis based on the confusion matrix for testing result. The best training result is performed by the GoogleNet pre-trained model, with an average running time of 7 minutes 14 seconds and an average accuracy of 98.53% \pm 1.27\%. The best model for classifying malaria image in the blood is the GoogleNet model with an accuracy value of 97.41%, precision 100%, recall 93.75%, specificity 100% and f-score 99.53%.
  • Publication
    Fuzzy Logic Cascaded Current Control of DC Motor Variable Speed Drive using dSPACE
    ( 2023-01-01)
    Ahmad Firdaus Ahmad Zaidi
    ;
    Ni L.P.
    ;
    Leong Jenn Hwai , Jenn
    ;
    Syahrul Ashikin Azmi
    ;
    Kamarulzaman Kamarudin
    ;
    Hasimah Ali
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Jusman Y.
    Two-wheel e-scooter falls under low power segment for Battery Electric Vehicle (BEV) and has gain more popularity in urban commuting. Most entry level e-scooter is still powered by DC motor due to low cost and ease of control. However basic open-loop DC Motor control employed through throttling is plugged with limited efficiency, precision, and range of speed control. Closed-loop control enables real time adjustment according to preset speed which becomes handy during auto cruising. To ensure good dynamic response, improved robustness and stable wide speed control range, a good control scheme for the motor is essential. In this project, a variable speed control scheme, namely fuzzy logic cascaded current control system was designed using MATLAB Simulink, comprising speed control loop and a current control loop 185 W Separately Excited Brushed DC Motor. The proposed control system was tested on hardware using dSPACE DS1104 platform. The system's output speed is obtained using an incremental encoder, while the output current is measured with a current sensor. Subsequently, the control system's stability, robustness, and dynamic performance were evaluated by driving the system on 120 W electrical load at varying speed. The system performance has proved superior to closed-loop by 70% on low speed ripple reduction and is on par with PI cascaded current control scheme.
      1
  • 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%.
      20  2
  • Publication
    The Classification System uses a Support Vector Machine and a Decision Tree Method Based on X-Ray Images for Spinal Abnormalities
    ( 2023-01-01)
    Anna Nur Nazilah C.
    ;
    Hasimah Ali
    ;
    Jusman Y.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Siddik I.R.
    ;
    Yusof M.I.
    A prevalent form of disorder that effects the vertebrae is a spinal disorder. X-ray technology is frequently used by medical professionals to detect abnormalities in the human body that are not visible to the unaided eye. Spinal ailment. Using the Hu moment invariant and machine learning, this study develops a system capable of feature extraction and spinal anomaly classification. The Hu Moment Invariant technique is used to derive seven moments (features) that describe an object. A support vector machine (SVM) identifies the optimal hyperplane in the input space that separates two classes. A decision tree (DT) is a technique for predicting the future by constructing a classification or regression model in the shape of a tree. Using the DT -Fine classification model derived from the Hu-Moment extraction results, the system can classify the newly developed research data in 1 minute with an accuracy of 88.2 % (highest) and 0.885782 seconds of feature extraction.
      5  24