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
    Analysis of Thermal Comfort Among Workshop Users: At TVET Technical Institution
    ( 2024-02-01)
    Alias N.M.
    ;
    Umar Kassim
    ;
    Sinar Arzuria Adnan
    ;
    Nur Hidayah Ahmad Zaidi
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Yusoff M.N.
    ;
    Sk Muiz Sk Abdul Razak
    Thermal comfort is a part of indoor environmental quality that should be considered to ensure the occupants' well-being. Unconducive buildings not only bring occupants discomfort but also tend to affect health, disrupt the process of teaching and learning, and reduce work productivity. Thus, this study determines the thermal condition of existing workshop buildings used in Technical and Vocational Education and Training (TVET) implementation. ASHRAE Standard 55 (2017) is referred to in the determination of thermal comfort involving objective measurements and subjective measurements. Observation methods of environmental variables such as air temperature, radiant temperature, air velocity, relative humidity is observed. Evaluations of comfort are based on occupant surveys and environmental measurements. A total of 257 people completed a questionnaire distributed at three technical institutions in Kedah, Malaysia. According to the findings, the average thermal sensation vote is 1.85, which leads to 66.5% of respondents feeling discomfort. Meanwhile, the adaptive model analysis showed that the workshop environmental conditions were out of the comfort zone and did not comply with the ASHRAE 55 standard. Hence, the thermal discomfort factors from the occupants' perspective were identified and widely discussed. As a result, the research findings will benefit parties involved in new building construction or existing building renovations to improve indoor air quality.
  • Publication
    Rhinitis phototherapy prototype with timer based on light energy
    ( 2024-05-01)
    Erika Loniza
    ;
    Mita Junita
    ;
    Yessi Jusman
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Kurnia Chairunnisa
    The set of timers in using phototherapy is major problem which has to be resolved to get a good performance of rhinitis phototherapy. This research aims to develop a prototype of phototherapy for allergic rhinitis, incorporating a timer based on light energy. The prototype utilizes a laser diode as a visible light source, specifically with a wavelength of 650 nm. The recommended safe and effective dose of light energy ranges from 1 to 10 Joules, which has been converted into minutes. Measurement tests indicate an average wavelength of 652.40 nm for the right laser, with a measurement uncertainty of ±0.11, and 653.23 nm for the left laser, with a measurement uncertainty of ±0.05. The laser diode source has an average voltage of 1.91 volts and an average current of 1.89 milliamperes, with a measurement uncertainty of ±0.00 and ±0.01, respectively. Additionally, the average discrepancy in the timer is 0.082 minutes for the 10-minute setting and 0.082 minutes for the 20-minute setting. These results confirm the effectiveness and suitability of the developed tool for practical use. The proposed method was useful for rhinitis therapy by using light energy.
  • 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
    IoT Based Smart Betta Fish Monitoring system with fish fatality prediction.
    ( 2023-01-01)
    Julida N.L.
    ;
    Othman S.M.
    ;
    Mohd Nasir Ayob
    ;
    Rahim N.A.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Muhamad Safwan Muhamad Azmi
    ;
    Hashim M.S.M.
    ;
    Talib M.T.M.
    ;
    Khalid N.S.
    This study enlightens the importance of rearing water quality to Betta fish health. A water quality monitoring system was developed based on water quality parameters namely water pH, temperature (°C) and TDS level (ppm). Fuzzy Logic Algorithm was applied to predict the possibility of the fish to get infected by the disease using combination of the water quality parameters value. Graphical User Interface (GUI) was developed to test the efficiency of the fish disease prediction system using fuzzy logic algorithm before the fuzzy rule been embedded to the IOT system. Arduino Uno Wi-Fi R2.0 and Blynk Apps used for enabling the system to update the aquarium water quality to owner in real-time. Hydroponic technology implemented in this project for recirculate rearing water inside the fish tank. Theoretically, the aquaponic system will help regulate the water tank parameters in optimum range and Betta Splendens should be free from all diseases.
  • 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
    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.
  • 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
    Recognition of different utility pipes size of ground penetrating radar images at different penetration depth
    ( 2024-02-08)
    Nasri M.I.S.
    ;
    Hasimah Ali
    ;
    Zaidi A.F.A.
    ;
    Shukor S.A.A.
    ;
    Ahmad M.R.
    ;
    Amran T.S.T.
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Othman S.M.
    ;
    Elshaikh M.
    Ground Penetrating Radar (GPR) is a geophysical locating method that uses radio waves to capture images below the surface of the ground in a minimally invasive way. It also requires two main essential equipment which is a transmitter and a receiving antenna. To address the problem, this project proposed the hyperbolic recognition of different utility pipes of GPR images at different level of penetration depth. In this framework, the raw data of GPR images were firstly to be pre-processed. The grayscale images were cropped, resized, and enhanced to increase the contrast of the features of the image. Then, the pre-processed GPR images were extracted using the Histogram of Oriented Gradient (HOG) method with three different windows. The extracted HOG features were then used as input to the k-Nearest Neighbor classifier. A series of experiments has been conducted using 10-fold cross-validation technique for training and testing the GPR data. Based on the result obtained, it shows that at depth 20cm the average accuracy is about 99.87%, whereas at depth 40cm the average accuracy achieved 100%. Thus, the result shows that the extracted HOG features exhibit the significant information of hyperbolic signature of different pipe size with different depth of buried object. Therefore the results seem promising in recognizing the hyperbolic of utilities.
  • 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.