TBA

1. Norsamsinar Samsudina, Ahmad Zainal Abidin Abd Razak, Julian Osman, Thuraiya Zakaria and Nazrina Aziz (2021). The Effect of Empathy on Students’ Motivation in Social Entrepreneurship among Undergraduates. Turkish Journal of Computer and Mathematics Education.

   Abstract: This study aimed to investigate the effect of empathy on students’ motivation in social entrepreneurship among undergraduates. A total of 300 students from 15 selected public universities in Malaysia under the Entrepreneurial Act Us Program (Enactus) were involved in this research. A set of questionnaires was utilized to collect the data. Descriptive statistics such as mean and standard deviation were used to identify the level of students’ motivation towards social entrepreneurship and empathy. Inferential statistics including correlation and simple linear regression were used to test the research hypotheses of the research. The results show that there is a significant effect between empathy and students' motivation for social entrepreneurship.

2. Mohd Azri Pawan Teh, Nazrina Aziz and Zakiyah Zain (2021). A new method in designing group chain acceptance sampling plans (GChSP) for generalized exponential distribution. International Journal of Quality & Reliability Management.

   Abstract: This paper introduces group chain acceptance sampling plans (GChSP) for a truncated life test at preassumed time by using the minimum angle method. The proposed method is an approach, where both risks associated with acceptance sampling, namely consumers’ and producer’s risks, are considered. Currently, the GChSP only considers the consumer's risk (CR), which means the current plan only protects the consumer not the producer since it does not take into account the producer's risk (PR) at all. There are six phases involved when designing the GChSP, which are (1) identifying the design parameters, (2) implementing the operating procedures, (3) deriving the probability of lot acceptance, (4) deriving the probability of zero or one defective, (5) deriving the proportion defective and (6) measuring the performance. The findings show that the optimal number of groups obtained satisfies both parties, i.e. consumer and producer, compared to the established GChSP, where the number of group calculated only satisfies the consumer not the producer. There are three limitations identified for this paper. The first limitation is the distribution, in which this paper only proposes the GChSP for generalized exponential distribution. It can be extended to different distribution available in the literature. The second limitation is that the paper uses binomial distribution when deriving the probability of lot acceptance. Also, it can be derived by using different distributions such as weighted binomial distribution, Poisson distribution and weighted Poisson distribution. The final limitation is that the paper adopts the mean as a quality parameter. For the quality parameter, researchers have other options such as the median and the percentile. The proposed GChSP should provide an alternative for the industrial practitioners and for the inspection activity, as they have more options of the sampling plans before they finally decide to select one. This is the first paper to propose the minimum angle method for the GChSP, where both risks, CR and PR, are considered. The GChSP has been developed since 2015, but all the researchers only considered the CR in their papers.

3. Muhamad Syafiq Abdul Ghani, Norhaslinda Zainal Abidin, Rosshairy Abd Rahman, Antoni Wibowo and Azatuliffah Alwi (2021). Modelling the Impact of Mobile Application Adoption on the Taxi Demand: An Application of a System Dynamics Approach. International Journal of Interactive Mobile Technologies.

   Abstract: The improvement of technology brings a significant impact on transportation industries. The taxi industry has undergone tremendous changes with the existent of e-hailing service in the industry. Due to the introduction of mobile applications, e-hailing service takes part to compete in the market. The government has given priority to overcome the problem by introducing travel demand strategies that focus on mitigating the demand competition between the taxi and e-hailing services. One of the strategies is the adoption of a mobile application in taxi service. This paper aims to develop a system dynamics model to analyse mobile application adoption’s impact on customers’ demand on the mode share of taxi and e-hailing services as a measured output. System dynamics is a decision-experimentation method that creates a learning environment in which policymakers gain a better understanding of how the system will respond to their decisions and the potential unintended consequences of decisions. With the developed SD model, the feedback relations between mobile application adoptions on the output of taxi demand can be observed. Furthermore, the demand competition between the taxi and e-hailing services can be minimised using this SD model. The result shows that, by implementing the usage of the mobile application in taxi services, more users will be attracted to use the taxi service. With that service option, users will shift their attraction from e-hailing to taxi service, which is then able to minimise the demand competition. This research can benefit policymakers and authorities in the department of transportation to serve as a planning tool so that the demand of taxi and e-hailing services in Malaysia with the adoption of mobile application in taxi service can be predicted.

4. Waqar Hafeez and Nazrina Aziz (2021). Bayesian Modified Group Chain Sampling Plan For Binomial Distribution Using Beta Prior Through Quality Region. International Journal of Productivity and Quality Management.

   Abstract: In this article, we introduced Bayesian Group Chain Sampling Plan (BGChSP) using different combination of parameters. In acceptance sampling plan, the random fluctuations can be describe in the selection of distribution Bayesian approach which is based on prior process history. We apply beta distribution as a suitable prior distribution. By considering consumer’s and producer’s risks, we consider Probabilistic and Indifference Quality Regions for the specified AQL and LQL. For the selection of parameters in BGChSP, Maximum Allowable Percent Defectives (MAPD) is also considered.

5. Mohd Azri Pawan Teh, Nazrina Aziz & Zakiyah Zain (2021). New Group Chain Acceptance Sampling Plans (NGChSP-1) using Minimum Angle Method for Generalized Exponential Distribution. Sains Malaysiana.

   Abstract: The established group chain acceptance sampling plans (GChSP-1) functions with five acceptance criteria, while the modified group of chain acceptance sampling plans (MGChSP-1) operates with three acceptance criteria. Since the acceptance criteria affect the performances of the sampling plans, therefore, this article suggests a balanced approach by introducing a new group of chain acceptance sampling plans (NGChSP-1), where it functions with four acceptance criteria. The NGChSP-1 is developed by using minimum angle method which caters for producer’s and consumer’s risks. The generalized exponential distribution is selected as the lifetime distribution and the simulation for the NGChSP-1 is conducted at various values of design parameters using the Scilab programming. The finding shows that the optimal number of groups and the corresponding smallest theta for NGChSP-1 are smaller compared to those for the GChSP-1. For illustration purposes, the NGChSP-1 is then applied to real data of air conditioning equipment.

6. Mangshor, N. N. A., Ibrahim, S., Sabri, N., & Kamaruddin, S. A. (2021). Students’ learning habit factors during COVID-19 pandemic using multilayer perceptron (MLP). International Journal of Advanced Technology and Engineering Exploration, 8(74).

   Abstract: Rapid dissemination of coronavirus disease 2019 (COVID-19) across the globe has necessitated the introduction of social distance interventions to slow the spread of the disease. Online learning has become essential, considering the implications of this virus to be spread among the students during physical classes. Hence, educational institutions have shifted the traditional physical classes to online classes. Due to this implementation worldwide, a study on student learning habits is crucial to analyse students learning habits as it is one of the main factors that affecting students’ performance in learning. Fifteen independent variables as inputs to one of the well-known Artificial Neural Network algorithms, Multilayer Perceptron (ANN-MLP) has been used to investigate the student’s learning habit factors during the COVID-19 pandemic. Through analysing original survey data from 420 secondary students (Grade 6-12) in Hanoi shows that the ANN-MLP model is stable for both ANN-MLP optimization algorithms which are for Adjusted Normalized, to be concise. The hours spend for self-learning before COVID-19 is observed to be the most influential factors of student’s learning habit during COVID-19 pandemic. Moreover, the promising Sum of Squares Error (SSE) and Relative Error (RE) values obtained signify that the ANN-MLP model is appropriate in identifying the student’s learning habit factors during COVID-19 pandemic.

7. Ibrahim S, Kamaruddin SA, Nordin MR, Shari AA, Sabri N, Samah KA. (2021). Global geographical climate impacts on the spread and death of COVID-19 in Asia and America. International Journal of Advanced Technology and Engineering Exploration. DOI:10.19101/IJATEE.2020.S1762133

   Abstract: A viral infection which is named as Coronavirus disease 2019 (COVID-19) is triggered by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). To date, almost two million cases and over 100,000 deaths from the disease caused by this virus were reported worldwide. The environmental and meteorological factors are claimed to stimulate the spread of the virus in which the transmissibility in terms of climatic fluctuations increases exponentially with high humidity and low temperature. In an attempt to understand this epidemic, there is a need to investigate the factors that could impact the spread and death of COVID-19. We, therefore, proposed to investigate global geographical climate impacts on the COVID-19 spread and death in Asia and America. The Artificial Neural Network (ANN) is a network that seeks to replicate neuronal functionality in the human brain. It is the preferred instrument for several predictive applications of data mining, due to its strength, versatility, and simplicity. A dataset of COVID-19 cases and deaths revealed from 49 states in America and 41 countries in Asia during April 2020 were tested. Nine covariates were used in the networks which are Cases, Death, High Temperature, Low Temperature, Average Temperature, Population, and Percentage of Cases over Population, Percentage of Death over Population, and Total Cases. Based on the analysis conducted, the global geographic climate is observed to have the least impacts on the COVID-19 spread and death in Asia and America particularly. However, different results could be reflected by different datasets used in the future.

8. Teh Raihana Nazirah Roslan, Chee Keong Ch’ng, Francis Chuah (2021). Understanding Students’ Intention to Engage in Deep Learning: Application of the Theory of Planned Behaviour. Elementary Education Online. doi:10.17051/ilkonline.2021.04.54

   Abstract: This paper examines students’ intention to engage in deep learning with the aim to understand them better. Majority of students practice surface learning approach, defined as having the intention to only meeting the minimum requirements by memorizing important information. Based on the Theory of Planned Behavior, we relate the components of this theory with our main quest of students’ intention to engage in deep learning, where the attainable predictors are students’ attitude, subjective norm, and perceived behavioral control. Our online survey, which was conducted and analyzed both statistically and descriptively, revealed that our students are deep learners. Our model was also found significant, with all three predictors were positive and significantly contributing to students’ intention to engage in deep learning. Nonetheless, detailed analysis suggests that none of the predictors appeared to have a stronger effect over the others. The findings from this study confirm the applicability of the Theory of Planned Behavior in explaining students’ intention to engage in deep learning. The findings also provide educators with the required knowledge to better design their curriculum with deep learning approaches.

9. Chia Ming Hong, Chee Keong Ch’ng, Teh Raihana Nazirah Roslan (2021). Application of Decision Tree in classifying secondary school students’ tendencies to choose TVET in Malaysia. Turkish Journal of Computer and Mathematics Education (TURCOMAT). 

   Abstract: The wave of Industry Revolution (IR 4.0) highlights the importance of technology in our life. The demand for technologist and skilled workers in Technical and Vocational Education and Training (TVET) are increasing day by day due to their expertise. TVET provides a platform for formal and non-formal learning to equip the youngsters in contributing to the development of a prosperous and inclusive nation. Moreover, TVET promises bright job prospects especially in fulfilling the manpower demand of IR 4.0. However, students in Malaysia currently are not fully aware of the existence of TVET, since the number of students who join TVET are still below expectation. Therefore, the main objective in this study is to develop the best TVET model to classify the students’ tendency in choosing TVET after completing secondary school. From the literature, five main factors that hinder students’ interest in joining TVET are recognized, namely students’ interest, parents, society, TVET instructors and employers. In this study, 428 secondary school students from Kedah (Malaysia) are involved as respondents. Different types of decision tree models are developed based on the algorithms and the splitting criteria. Altogether, there are 15variables derived from 5 main affecting factors mentioned above to determine the tendency of joining TVET. Consequently, the best TVET classifier with the misclassification rate of 0.2919 is selected, to predict the tendency of students who will be joining TVET in future. Our findings revealed that the variable of “Stream” plays as the primary and trifling roles. This classifier is beneficial in assisting the government to achieve the aim of upholding TVET in Malaysia.

10. Friday Zinzendoff Okwonu, Nor Aishah Ahad, Joshua Sarduana Apanapudor and Festus Irismisose Arunaye (2021). Robust Multivariate Correlation Techniques: A Confirmation Analysis using Covid-19 Data Set. Pertanika Journal of Science & Technology.

    Abstract: Robust multivariate correlation techniques are proposed to determine the strength of the association between two or more variables of interest since the existing multivariate correlation techniques are susceptible to outliers when the data set contains random outliers. The performances of the proposed techniques were compared with the conventional multivariate correlation techniques. All techniques under study are applied on COVID-19 data sets for Malaysia and Nigeria to determine the level of association between study variables which are confirmed, discharged, and death cases. These techniques’ performances are evaluated based on the multivariate correlation (R), multivariate coefficient of determination (R²), and Adjusted R². The proposed techniques showed R=0.99 and the conventional methods showed that R ranges from 0.44 to 0.73. The R² and the Adjusted R² for proposed methods are 0.98 and 0.97 while the conventional methods showed that R equals 0.53, 0.44, and 0.19 whereas Adjusted R² equals 0.52, 0.43, and 0.18, respectively. The proposed techniques strongly affirmed that for any patient to be discharged or die of the Covid-19, the patient must be confirmed Covid-19 positive, whereas the conventional method showed moderate to very weak affirmation. Based on the results, the proposed techniques are robust and show a very strong association between the variables of interest than the conventional techniques.

11. Shafaf Ibrahim, Saadi Ahmad Kamaruddin, Nur Nabilah Abu Mangshor, Ahmad Firdaus Ahmad Fadzil (2021). Performance Evaluation of Multi-Layer Perception (MLP) and Radical Basis Function (RBF): Covid-19 Spread and Death Contributing Factors. International Journal of Advanced Trends in Computer Science and Engineering.

    Abstract: The Artificial Neural Network (ANN) is an Artificial Intelligence technique which has the ability to learn from experiences, enhancing its performance by adapting to the environmental changes. The key benefits of neural networks are the prospect of processing vast quantities of data effectively, and their ability to generalize outcomes. Considering the great potential of this technique, this paper aims to establish a performance evaluation of Multilayer Perceptron (MLP) and a Radial Basis Function (RBF) networks in investigating the contributing factors for COVID-19spread and death. The RBF and MLP networks are typically used in the same form of applications, however, their internal calculation structures are different. A comparison was made by using a dataset of COVID-19 cases in 41 Asia countries during April 2020. There are nine contributing factors which acted as the covariates to the network such as Cases, Deaths, High Temperature, Low Temperature, Population, Percentage of Cases over Population, and Percentage of Death over Population, Average Temperature, and Total Cases. The results obtained from the testing sets indicated that the two neural structures were able to investigate the COVID-19 spread and death contributing factors. Nevertheless, the RBF network indicated a slightly better performance than the MLP.

12. Ibrahim, S.; Kamaruddin, S. A.; Sabri, N.; Samah, K. A.; Noordin, M.; Shari, A.(2021). The Influence of Global Geographical Climate towards Covid-19 Spread and Death. International Journal of Advanced Trends in Computer Science and Engineering.

    Abstract: As the world’s coronavirus disease 2019 (COVID-19) case total and death toll continue to climb, an increasing data collection and analysis are providing insights into the pandemic. Although outbreaks continue to develop rapidly, and researchers' understanding of the virus is increasing, a consensus is emerging on certain main aspects of the spread, symptoms, and deadliness of the virus. Enormous global data distribution on COVID-19 is made available online with a combination of global climate data, which creates an opening for further analysis to be conducted. To date, the global climate change has been studied widely, particularly regarding its influences on the distribution of species. This reflects the need for an analysis that is best suited to big data analysis which offers high performance and efficiency in understanding this pandemic issue. The state-of-art in data mining and statistics areas show that the adaptation of these methods could be the most suitable candidate for this purpose. We, therefore, proposed to investigate the influences of the global geographical climate towards the COVID-19 spread and death using a technique of Artificial Neural Network (ANN). It is believed that the proposed study could introduce a new suggestive strategy in improving the precaution measures, enhancing the new normal living activities, and to increase the performance scalability of big data processing comprehensively.

13. Faiqah Mohamad Fudzi, Zahayu Md Yusof and Masnita Misiran (2021). Rainfall Forecasting With Time Series Model In Alor Setar, Kedah. Universiti Malaysia Terengganu Journal of Undergraduate Research.

    Abstract: The prediction of rainfall on monthly and seasonal time scales is not only scientifically challenging but is also important for planning and devising agricultural strategies. In this paper, the study is conducted to examine the pattern of monthly rainfall in Alor Setar, Kedah within ten years which is from 2008 to 2018. This paper considered a model based on real data that obtained from Department of Meteorology Malaysia. This study indicates that the monthly rainfall in Alor Setar has a seasonal and trend pattern based on yt vs t plotting, autocorrelation function and Kruskal Wallis Test for seasonality. The examined rainfall time-series modelling approaches include Naïve Model, Decomposition Method, Holt-Winter’s and Box-Jenkins ARIMA. Multiplicative Decomposition Method was identified as the best model to forecast rainfall for the year of 2019 by analysing the previous ten-year’s data (2008-2018). As a result from the forecast of 2019, October is the wettest month with highest forecasted rainfall of 276.15mm while the driest month is in February with lowest forecasted rainfall of 50.55mm. The model is therefore adequate and appropriate to forecast future monthly rainfall values in the catchment which can help farmers to plan their farming activities ahead of time.

14. Ijlal Mohd Diah and Nazrina Aziz (2021). Mapping of Pneumonia Disease in Malaysia Using Poisson-Gamma Model. Annals of the Romanian Society for Cell Biology.

    Abstract: Bayesian models have been used as one of the estimation techniques for smoothing relative risks estimation for disease mapping. One of the most common uses of Bayesian methodology in disease mapping is Poisson-gamma model. This paper begins with a brief introduction of pneumonia disease. Then followed by a review of methodology used in this paper that is Poisson-gamma model which used to estimate the values of relative risk. Then, the results will be shown into a map to show pneumonia risk areas. The risk areas can be shown clearly through disease map. Thus, the objective of this study is to estimate the value of relative risk for pneumonia based on Poisson-gamma model. Pulau Pinang, Perak, Selangor, Kuala Lumpur & Putrajaya, Sabah, Labuan and Sarawak are categorized as low-risk of pneumonia occurrence, while Terengganu and Negeri Sembilan categorized as high-risk areas.

15. Shafaf Ibrahim, Saadi Ahmad Kamaruddin, Nur Nabilah Abu Mangshor and Ahmad Firdaus Ahmad Fadzil (2021). A Study of Covid-19 Spread and Death Contributing Factors in America using Multi-Layer Perception (MLP) and Radial Basis Function (RBF). European Journal of Molecular & Clinical Medicine.

    Abstract: In recent years, Artificial Neural Networks (ANN) was widely implemented for developing predictive and estimation models to estimate the needed parameters. As the Coronavirus disease 2019 (COVID-19) case numbers are rising internationally as uncontrolled outbreaks, it is important to better understand what factors promote the super spreading events. In this paper, the use of Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) of ANN for COVID-19 spread and death contributing factors in America was described. A comparison was made by using a dataset of COVID-19 cases and deaths reported from 49 states in America during April 2020. Seven covariates used in the networkwhich are High Temperature, Low Temperature, Average Temperature, Population, Percentage of Cases over Population, Percentage of Death over Population, and Total Cases. However, the performance of MLP and RBF networks may be evaluated relatively similar. It was found that both MLP and RBF proved that the Population, Percentage of cases over population, and Total cases are the most contributing factors towards COVID-19 spread and death in America particularly.

16. Nurulhuda Muhamad, Zahayu Md Yusof and Masnita Misiran (2021). Crime Index Trends and Patterns in Kelantan. Data Analytics and Applied Mathematics.

    Abstract: Crimes are a social nuisance and has become major anxiousness to the society where it involves the safety of the people in a country. This paper provides crime index overview in Kelantan from 2017 to 2019 that consists of both violent crimes and property crimes. The violent crimes involve murder, rape, robbery and voluntarily injury cases. Meanwhile, property crimes include house break-in theft, vehicles theft and other theft (pickpocketing, snatch theft and etc.). The purpose of this paper is to study the relationship between age, gender, ethnic and district with the crime index. In addition, the objective is to identify and get better understanding of the most common crime index and also to identify which area has the highest crime index in Kelantan. A total of 5,569 cases were reported within this three-year period and the data were collected from Kelantan Contingent Police Headquarters. The descriptive analysis, spearman’ rho correlation and multiple regression analysis were performed, and the findings were then illustrated via graphs and tables. The major results have shown that Kota Bharu has the highest crime index and age, gender and district has significant relationship with crime index.

17. Nurul Nabilah Mohd Razif, Masnita Misiran, Zahayu Md Yusof & Hasimah Sapiri (2021). Factors that Influence the Perceived Risk towards E-Wallet Platform in Malaysia. Malaysian Journal of Youth Studies.

    Abstract: Among mostly used platform in fintech industry is the e-wallet service. As technology progresses, doing financial transactions through online platform is also susceptible to certain risk, e-wallet platform included. 231 young adults aged between 18 to 30 years old in Malaysia involved in this study. The data collected via online platform using snowball sampling. Exploratory factor analysis was executed to determine the appropriate perceived risk factors among young adult in e-wallet platform. The finding suggested seven factor that influence the perceived risk, with the factor most explained are Behavioural Intent, Perceived Security, Perceived Privacy Risk, Perceived Usefulness, Trust, Perceived Overall Risk and Perceived Performance Risk as the factor influenced the perceived risk towards e-wallet platform in Malaysia.

18. Tan Vyn Ni, Zahayu Md Yusof, Masnita Misiran and Siti Suzlin Supadi (2021). Assessing Youth Unemployment Rate in Malaysia using Multiple Linear Regression. Journal of Mathematics and Computing Science.

    Abstract: This study investigated the relationship between urbanization, inflation, gross domestic product and foreign direct investment towards youth unemployment in Malaysia. As youth unemployment has been increasing over the past decade, it is therefore crucial to decide which variables are the most critical in affecting Malaysia's youth unemployment rate as to ensure sustainable economic growth in the country. Data was obtained from Department of Statistics Malaysia and World Bank websites. Descriptive and multiple linear regressions were used in this study. The outcome revealed that foreign direct investment and gross domestic product growth are significant factors, while urbanization and inflation showed insignificant relationship towards the contribution to the youth unemployment rate.

19. Mirza Anwar Zainuddin, Lai Jun Jer, Muhamad Zulhelme Bakar, Kumaresan Palanisamy and Zahayu Md Yusof (2021). Evaluation of Students’ Satisfaction towards Instructor using Factor Analysis. Journal of Science and Mathematics Letters.

    Abstract: Student evaluation on instructors is used in almost every high institution throughout the world whether in university or even colleges. The objective of this paper is to examine the factors that contribute to the student's evaluation on instructors among undergraduate students at Northern University of Malaysia (UUM) by using factor analysis method. The respondents of this study were 153 undergraduate students at the School of Quantitative Science via convenience sampling and snowball sampling method. The questionnaire was distributed to the participants via Google Form regarding demographic information and evaluation of the instructor. The questionnaire evaluation of the instructor consisted of 31 questions in total and 153 responses were received. The factors were classified into dependent and independent variables to identify the significance of the variables. In this paper, the variables of Student Interest, Student-Instructor, Course Demands and Course Organization were grouped as independent factors while Instructor involvement as the dependent factor.

TBA