Sukhairi Sudin
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Sukhairi Sudin
Official Name
Sukhairi, Sudin
Alternative Name
Sudin, S.
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Scopus Author ID
56572705200
Researcher ID
GFW-2221-2022

Research Output

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  • Publication
    Cloud-based System for University Laboratories Air Monitoring
    ( 2020-09-21)
    Abu Hassan Abdullah
    ;
    Sukhairi Sudin
    ;
    Mustafa M.H.
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Khairul Azwan Ismail
    ;
    Muhammad Aizat Abu Bakar
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    Abdul Ghapar Ahmad
    ;
    Zahari Awang Ahmad
    ;
    Sara Yasina Yusuf
    Indoor air such as house, shopping complex, hospital, university, office and hotel should be monitor for human safety and wellbeing. These closed areas are prone to harmful air pollutants i.e. allergens, smoke, mold, particles radon and hazardous gas. Laboratories in university are special room in which workers (student, technician, teaching/research assistants, researcher and lecturer) conduct their works and experiment. The activities and the environment will generate specific air pollutant which concentration depending to their parameters. Anyone in the environment that exposure to these pollutants may affect safety and health issue. This paper proposes a study of development of a cloud-based electronic nose system for university laboratories air monitoring. The system consists of DSP33-based electronic nose (e-nose) as nodes which measure main indoor air pollutant along with two thermal comfort variables, temperature and relative humidity. The e-noses are placed at five different laboratories for acquiring data in real time. The data will be sent to a web server and the cloud-based system will process, analyse using Neuro-Fuzzy classifier and display on a website in real time. The system will monitor the laboratories air pollutants and thermal comfort by predict the pollutant concentration and dispersion in the area i.e. Air Pollution Index (API). In case of air hazard safety (e.g., gas spills detection and pollution monitoring), the system will alert the security by activate an alarm and through e-mail. The website will display the API of the area in real-time. Results show that the system performance is good and can be used to monitor the air pollutant in the university laboratories.
      43  2
  • Publication
    Cycling performance prediction based on cadence analysis by using multiple regression
    ( 2021-12-01)
    Sukhairi Sudin
    ;
    Aziz Naim Abdul Aziz
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Nurul Syahirah Khalid
    ;
    Ismail Ishaq Ibrahim
    This project examined the influence of the cadence, speed, heart rate and power towards the cycling performance by using Garmin Edge 1000.Any change in cadence will affect the speed, heart rate and power of the novice cyclist and the changes pattern will be observed through mobile devices installed with Garmin Connect application. Every results will be recorded for the next task which analysis the collected data by using machine learning algorithm which is Regression analysis. Regression analysis is a statistical method for modelling the connection between one or more independent variables and a dependent (target) variable. Regression analysis is required to answer these types of prediction problems in machine learning. Regression is a supervised learning technique that aids in the discovery of variable correlations and allows for the prediction of a continuous output variable based on one or more predictor variables. A total of forty days' worth of events were captured in the dataset. Cadence act as dependent variable, (y) while speed, heart rate and power act as independent variable, (x) in prediction of the cycling performance. Simple linear regression is defined as linear regression with only one input variable (x). When there are several input variables, the linear regression is referred to as multiple linear regression. The research uses a linear regression technique to predict cycling performance based on cadence analysis. The linear regression algorithm reveals a linear relationship between a dependent (y) variable and one or more independent (y) variables, thus the name. Because linear regression reveals a linear relationship, it determines how the value of the dependent variable changes as the value of the independent variable changes. This analysis use the Mean Squared Error (MSE) expense function for Linear Regression, which is the average of squared errors between expected and real values. Value of R squared had been recorded in this project. A low R-squared value means that the independent variable is not describing any of the difference in the dependent variable-regardless of variable importance, this is letting know that the defined independent variable, although meaningful, is not responsible for much of the variance in the dependent variable's mean. By using multiple regression, the value of R-squared in this project is acceptable because over than 0.7 and as known this project based on human behaviour and usually the R-squared value hardly to have more than 0.3 if involve human factor but in this project the R-squared is acceptable.
      3  16
  • Publication
    Development of aquaculture water quality real-time monitoring using multi-sensory system and internet of things
    ( 2021-12-01)
    Abu Hassan Abdullah
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Sukhairi Sudin
    ;
    Zahari Awang Ahmad
    ;
    Ahmad I.
    ;
    Norlida Abu Bakar
    ;
    Shafie Omar
    ;
    Sulaiman S.F.
    ;
    Mohd Hishamuddin Che Mat
    ;
    Nurul Afidah Umoruddin
    ;
    Johari B.H.
    Water quality is an important parameter for the health and growth of aquatic species in aquaculture farming system. The threshold values of the water main parameters should be monitored continuously. Contaminated aquaculture water will affect the health, growth and ability of animals to survive. In addition, it will also affect the harvesting yields based on the number and size of the animals. To overcome this problem, the main water parameters, namely temperature, pH, Dissolved Oxygen and Electrical Conductivity are monitored in real-time using a multi-sensory system and the internet of things. Data is acquired by a developed instrument and transmitted wirelessly via a GPRS/GSM module to a web server database. The data obtained are analyzed and monitored through the website and in real-time. Therefore, corrective action could be taken immediately for contaminated water, indicated by water parameters out of range. The system also provides an early signal to farmers based on a specific range of water quality parameters values. This will help farmers make adjustments to ensure appropriate water quality for the aquaculture system.
      7  43
  • Publication
    Intelligent irrigation system using rain water harvesting system and fuzzy interface system
    ( 2021-12)
    Azremi Abdullah Al-Hadi
    ;
    Sukhairi Sudin
    ;
    Ahmad Z.A
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    I Ahmad
    ;
    Fadhilnor Abdullah
    ;
    Wan Mohd Faizal Wan Nik
    ;
    A. Deraman
    ;
    N. M Maliki
    ;
    S R S Kamaruzaman
    ;
    S R Romle
    Shortage of water has become a predominant problem all over the world as water plays an important role in agriculture, domestic and industry. In certain parts of the world, farmers face problems watering their crops especially during the dry season. Limited water resources with low efficiency greatly affect crop growth. Therefore, this study proposes an intelligent irrigation system using Rain Water Harvesting (RWH) and Fuzzy Interface System (FIS) for crops watering process. The RWH is a system that collects, centralises and stores rainwater, while the FIS uses temperature and soil moisture sensors to determine the time and amount needed for the watering process. Thus, the intelligent irrigation system will ensure the process of watering the crops to be efficient. The results of this study show that FIS can analyse temperature and soil moisture data, which improves the efficiency of crops watering process and the use of RWH will make it sustainable. The developed project is currently operating at the Institute of Sustainable Agro Technology, i.e. a university-owned agricultural research institute.
      1  18
  • Publication
    Development of a Common Waste Combustion System for Generating Electricity at Remote Area
    ( 2023-01-01)
    Zaini A.S.A.
    ;
    Sukhairi Sudin
    ;
    Haris F.A.
    ;
    Nurul Syahirah Khalid
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Muhamad Khairul Ali Hassan
    ;
    Ibrahim I.I.
    Malaysia's daily amount of municipal solid waste (MSW) has rapidly increased. This causes the landfills number to increase due to inadequate waste management systems. Apart from that, Malaysia depends on non-renewable resources for electricity generation which could have a significant effect on the environment. Therefore, this study proposed to reduce landfills in Malaysia in a proper way and supply electricity using municipal solid waste as a renewable resource. In this study, the combustion of municipal solid waste (MSW) produces steam, which will rotate a turbine that is connected to a dynamo. Then, the energized dynamo will supply electricity to appliances including a direct current motor. The motor shaft then rotates the dynamo shaft in the pulley system which causes the electricity to flow in a closed loop. In this system, a pressurized container is crucial to produce sufficient steam. Based on the experimental setup, it was observed that continuous electricity was successfully achieved by looping the system using a pulley on the dynamo and motor.
      1  21
  • Publication
    Potential of Near-Infrared (NIR) spectroscopy technique for early detection of Insidious Fruit Rot (IFR) disease in Harumanis mango
    ( 2021-12-01)
    Noor Shazliza Zakaria
    ;
    Abu Hassan Abdullah
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Muhamad Khairul Ali Hassan
    ;
    Mohd Nazri Abu Bakar
    ;
    Saad A.R.M.
    ;
    Sukhairi Sudin
    ;
    Ibrahim M.F.
    Harumanis mango 'Insidious Fruit Rot'(IFR), is one of the common issues that hampered the fruit quality and consequently lowered the premium value of Harumanis Mango. Physically and visually the affected fruit does not show any attributes that indicates the presence of IFR on any part of the fruit until it has been cut open. This paper investigates the feasibility of a non-destructive method to screen the Harumanis mango from IFR using near-infra red light and artificial neural network. A common NIR light emitting diodes of 1000nm wavelength was used as the light source to emit NIR light while a photodiode was used to measure the intensity of the reflected NIR light from Harumanis mango. Early detection of IFR were done manually by local expert using acoustic method by flicking fingers to detect any abnormality inside the fruit. Sample data on NIR Spectroscopy reflectance results of 120 samples were used to classify the presence of IFR using neural network. Mean value of NIR reflectance of RBG for Harumanis mango with an incidence of Insidious Fruit Rot are R= 0.651, G= 0.465 and B=0.458, while without IFR are R = 0.211, G=0.15 and B=0.146. Using MATLAB's neural network training tool, a training set regression was obtained with an accuracy value of 0.9805 for prediction of IFR, thus this value is very high in accuracy.
      48  9