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
    Clasification of Malaria images in thropozoid stages using deep learning models
    (IEEE, 2024-03)
    Wikan Tyassari
    ;
    Yessi Jusman
    ;
    Novian Dwi Payana
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Zeehaida Mohamed
    The risk of malaria infection is very high, especially for people living in eastern Indonesia, such as Papua, Maluku, and Nusa Tenggara. In Indonesia there are several types of malaria parasite infected, Plasmodium Falciparum, Plasmodium Vivax, and Plasmodium Malaria. Identifying malaria at an early stage is an important to reduce the risk of death and find suitable treatment. However, identifying and diagnosing malaria is time consuming. Therefore, it is necessary to apply technology in detecting the class of malaria parasites. This study classified images of malaria parasites Plasmodium Falciparum, Plasmodium Vivax, and Plasmodium Malarie at the trophozoite stage using the deep learning pre-trained models AlexNet and Inception-V3. According to accuracy of training, Inception-V3 is the best deep learning model. The performance analysis result of inception is accuracy 98.98% ± 0.71%, precision 98.83% ± 1.44%, recall 98.83% ± 1.38%, specificity 99.11% ± 1.09%, and F-score 98.82% ± 0.83%. However, despite having lower accuracy and performance AlexNet have faster in computational training time.
  • Publication
    Evaluate of vest massage therapy with rotating pressure based on pre-experimental methods
    (Institute of Advanced Engineering and Science (IAES), 2025-04)
    Erika Loniza
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Yessi Jusman
    ;
    Zulkarnay Zakaria
    Many postpartum mothers complain that their milk production is too low to supply the baby’s needs. There are two essential substances in the milk: the prolactin hormone and the oxytocin hormone. Consequently, there are two ways to stimulate these hormones: massage techniques such as breast care and oxytocin massage. This study aims to design vest therapy devices to expedite breast milk production. With the use of vest therapeutic devices, it can be observed that the amount of breast milk production increases. This research uses a pre-experimental method in postpartum mothers, which uses the vest massage therapy and does not use the vest massage therapy. Accidental sampling was used as the sampling method for this study, and the data were analyzed using the independent t-test. It is hoped that making Vest therapy devices can facilitate breastfeeding for postpartum mothers with the aim that they can increase the amount of breast milk and supply the milk for the babies in the early stage of their life. The test result discovered an increase in breast milk volume in breastfeeding mothers by an average of 7.3 ml in postpartum mothers who used vest therapy equipment compared to the previous amount of milk produced.
  • 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.
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