Using Machine Learning classification methods to detect COVID-19 Status in Selangor District

Recently, Covid 19 pandemic has been recognized as a public health emergency of international concern. According to global COVID-19 infection data, the total number of cases is over 147 million, with over 3 million fatalities. Common model that use to predict binary outcome is Logistic regression model. However, the majority of the models have not been implemented widely by using ML approaches. Thus, the interest of this study has been coined to the Covid 19 cases prediction model that influence the extend of risk to urge Covid 19 infection. Therefore, this study addressed how to use four ML algorithms offered by Rapid Miner software tools to identify the optimum classification model. The results show that Decision Tree has been very promising to produce a high percentage of accuracy rate of 75.47% compared to other models. Further research on the data structure is necessary to be conducted in order to address problems like bias and an unbalanced dataset. In addition, new factors like vaccination status should be incorporated into the model to determine whether the respondent is at risk of contracting COVID 19 or not. © 2022 IEEE.

Student Readiness Factors for Online Distance Learning among Malaysian Public Universities during COVID-19: A Conceptual Model

Students’ academic performance will be positively affected by their readiness to learn. This study investigated the association between university students' learning readiness variables and academic achievement. Therefore, the purpose of this study is to identify student readiness factors that influence students' academic performance and to propose a conceptual model that will improve students’ academic performance in the ODL system. A set of self-administered questionnaires were distributed to 233 students attending public higher education institutions using a quantitative research approach. Using the Structural Equation Modelling (SEM) technique, the student's readiness was determined by investigating the cause-and-effect relationship between three readiness factors: (i) Efforts Expectancy (EE), (ii) Attitudes (ATT), and (iii) Facilitating Conditions (FC), towards the Performance Expectancy (PE) factor. The results suggested that Facilitating Conditions (FC) is the most influential readiness component and has a significant impact on students' learning acceptance, which directly increases their learning achievement. With great optimism, it is hoped that this study will aid MOHE and university decision-makers in gaining an understanding of the critical factors that drive the online distance learning (ODL) system. Implementing the proposed conceptual model will thereby simplify the acceptance of the system © 2022, Asian Journal of University Education.All Rights Reserved.