Hamzah Abdul Hamid
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Hamzah Abdul Hamid
Official Name
Hamzah, Abdul Hamid
Alternative Name
Hamid, Hamzah Abdul
Abdul Hamid, Hamzah
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Scopus Author ID
57192434954
Researcher ID
CUF-2323-2022

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  • Publication
    Problematic review on water tariff-pricing model and its relation with environment and climate change
    ( 2021-07-21)
    Mohammad Fadzli Ramli
    ;
    Hek T.K.
    ;
    Hamzah Abdul Hamid
    ;
    Mohd Syafarudy Abu
    The issue of water security, environment degrade and climate change has becoming more severe today and emergence actions are needed to mitigate the problem. The variability in climate change affected the water resources thus contributes to unstable and uneconomic water price. This problem is worsened by environmental and pollution impact thus led to irrational water tariff and price. Existing model only considers econometric values, but environmental and climate change is neglected from overall structure of water tariff and price. Furthermore, the non-structured water pricing model fails to educate water consumers about excessive use and water scarcity. Therefore, water tariff and pricing mechanisms need to accommodate the changing in environmental degradation and climate change for more rationale and relevant water tariff and price.
  • Publication
    The effect of different distance matrix on goodness-of-fit test for multinomial logistic regression
    (AIP Publishing, 2023)
    Hamzah Abdul Hamid
    ;
    Yap Bee Wah
    In a previous study, the performance of goodness-of-fit test based on clustering partitioning strategy for multinomial logistic regression has been investigated by using different type of clustering techniques. It is known that the clustering technique involves distance calculation to identify which cluster a data belongs to. There are several distance matrices available, but the most popular one is the Euclidean distance. Thus the previous study only considered the Euclidean distance in the process of clustering the data. In this study, the effect of different distance matrix has been investigated. Four different distance matrices, which are Maximum, Manhattan, Minkowski and Canberra were used to investigate the effect on the performance of the test. The results were then compared with the results from previous studies, which used Euclidean distance. The results show that Canberra distance produced different mean value and rejection rate while the other distance measurements produced the same mean value and rejection rate as Euclidean distance.
  • Publication
    The effect of divisive analysis clustering technique on goodness-of-fit test for multinomial logistic regression
    (Semarak Ilmu Publishing, 2025-06)
    Hamzah Abdul Hamid
    ;
    Yap Bee Wah
    ;
    Khatijahhusna Abdul Rani
    ;
    Xian Jin Xie
    The relationship between a categorical dependent variable and independent variable(s) are usually modelled using the logistic regression method. There are three types of logistic regression: binary, multinomial, and ordinal. When there is two cayegories of dependent variable, binary logistic regression is used while when there is more than two nominal categories of dependent variable, multinomial logistic regression is employed. Ordinal logistic regression is used when the dependent variable contains more than two ordinal categories. All regression models should be checked after being fitted to the data to see whether it matches the data or not. For multinomial logistic regression, there are numbers of goodness-of-fit test proposed and can be used to evaluate the fit of the model. One of the proposed tests is based on clustering partitioning strategy. Howevert, the proposed test only considered agglomerative nesting (AGNES) hierarchical clustering technique, which is Ward’s to group the data. The performance of the test using divisive analysis (DIANA) hierarchical clustering technique is remain unknown. Thus, this study attempts to examine the power of the test using divisive analysis clustering technique. Simulation technique is used to evaluate the performance of the test. The results showed that that the test using DIANA clustering technique has managed type I error and the mean are close to hypothesized values. It also has almost equivalent power with the test using Ward’s clustering technique in detecting omission of a quadratic term. However, the test using Ward’s clustering technique shows noticeably higher power in detecting omission of an interaction term.
  • Publication
    Biomechanical and ergonomics study of manual material handling during team lifting activity
    ( 2023-02-21)
    Latib N.L.M.
    ;
    Mohammad Shahril Salim
    ;
    Shahriman Abu Bakar
    ;
    Ahmad Faizal Salleh
    ;
    Nurhidayah Omar
    ;
    Hamzah Abdul Hamid
    ;
    Saidatul Ardeenawatie Awang
    ;
    Kamaruddin A.
    This project focused only on the joint contribution and ground reaction force that took place during the team lifting activity made up of two people. This present study hypothesized that the workplace variables such as the weight of loads, the height of load to be lifted and gender would affect the kinetics and kinematics variables. Eight healthy participants (BMI: 18.5 till 24.9 kg/m2) divided into four team where there are two groups of male and two groups of females with two individuals in each team have performed asymmetric lifting task under four different conditions which are two weights of loads (5 kg and 15 kg) and two level of lifting heights (participant knuckle and elbow height from ground). There are five Oqus cameras motion capture system (Pro Reflex infrared, Qualysis) to capture the participant motion, Qualisys Track Manager (QTM) software had been used to label the markers on participant body while the force plate had been used for data collection of ground reaction force throughout the lifting activities. The data collected from QTM converted into C3D file to be used in Visual 3D software to do bone modelling and analysis on ground reaction force, joint angle and joint moment. The results show that here was a statistically significant interaction between the effect on gender and load on joint angle, p=0.001 for hip. However, there is no statistically significant interaction between gender and load on right and left knee angle. Besides the two-way ANOVA was conducted that examine the effect of gender and load on joint moment. Thus, there is a statistically significant interaction between the effect on gender and load on joint angle, p=0.001 for all joint moment at both elbow and knuckle height. In term of ground reaction force, there was a statistically significant interaction effect between gender and load on the combined dependent variable during to-lift phase at in lifting phase position at elbow height and knuckle height, p=0.001 when using two-way MANOVA. Based on the results of this study, it was concluded that hip joint angle, hip and knee joint moment affected by gender and load while for ground reaction force are influenced by the variables of lifting height, lifting stage, gender and weight of loads.
      4  74
  • Publication
    Assessing the effect of different covariates distributions on parameter estimates for Multinomial Logistic Regression (MLR)
    (IOP Publishing, 2020)
    Hamzah Abdul Hamid
    ;
    Siti Raudhah Ismail
    ;
    Sahimel Azwal Sulaiman
    ;
    Nor Azrita Mohd Amin
    n fitting a multinomial logistic regression model, one of the most important part is estimating the parameter. In Multinomial Logistic Regression (MLR), Maximum Likelihood Estimation (MLE) method is used to estimate the parameters. MLE is the suitable method to be applied to the problems associated with categorical response variables since it has several benefits such as sufficiency, consistency, efficiency and parameterization invariance. This study investigates the different type of continuous distributions (normal, negatively skewed, positively skewed) on parameter estimation via Monte Carlo simulation. From the simulation result, it shows that as the sample size increases, the effect of covariate distribution reduces. The estimated parameter also less affected for model with normal covariate distribution. At sample size 300 and above, the estimated parameter with normal covariate distribution is considered as close to the true parameter value. Interestingly, for the positively skewed, the estimated parameter also obtained unbiased parameter at sample size 300 and above. However, for negatively skewed, it requires a larger sample size to get closer to the true parameter value. The estimated parameters deviate too far from the true parameter at small sample size. As expected, as sample size increases the parameter estimates for all distributions are getting close to the true parameter value. Lastly, the distribution for MLR with more than one covariate give the same effect as the MLR model with only one covariate on parameter estimations.
      10  2