Improving forecasts, particularly the accuracy, efficiency, and precision of time-series forecasts, is becoming critical for authorities to predict, monitor, and prevent the spread of the Coronavirus disease. However, the results obtained from the predictive models are imprecise and inefficient because the dataset contains linear and non-linear patterns, respectively. Linear models such as autoregressive integrated moving average cannot be used effectively to predict complex time series, so nonlinear approaches are better suited for such a purpose. Therefore, to achieve a more accurate and efficient predictive value of COVID-19 that is closer to the true value of COVID-19, a hybrid approach was implemented. Therefore, the objectives of this study are twofold. The first objective is to propose intelligence-based prediction methods to achieve better prediction results called autoregressive integrated moving average–least-squares support vector machine. The second objective is to investigate the performance of these proposed models by comparing them with the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average–support vector machine. Our investigation is based on three COVID-19 real datasets, i.e., daily new cases data, daily new death cases data, and daily new recovered cases data. Then, statistical measures such as mean square error, root mean square error, mean absolute error, and mean absolute percentage error were performed to verify that the proposed models are better than the autoregressive integrated moving average, support vector machine model, least-squares support vector machine, and autoregressive integrated moving average–support vector machine. Empirical results using three recent datasets of known the Coronavirus Disease-19 cases in Malaysia show that the proposed model generates the smallest mean square error, root mean square error, mean absolute error, and mean absolute percentage error values for training and testing datasets compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average–support vector machine models. This means that the predicted value of the proposed model is closer to the true value. These results demonstrate that the proposed model can generate estimates more accurately and efficiently. Compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average–support vector machine models, our proposed models perform much better in terms of percent error reduction for both training and testing all datasets. Therefore, the proposed model is possibly the most efficient and effective way to improve prediction for future pandemic performance with a higher level of accuracy and efficiency.

The Malaysian government implemented The Movement Control Order (MCO) on 18 March 2020 to control the spread of the COVID-19 outbreak. However, the third wave that started in September 2020 during the Recovery Movement Control Order (RMCO) phase saw a continuous increase in the number of cases. In this study, the exploratory spatial data analysis (ESDA) was used to analyse the existence of COVID-19 spatial clusters. Moran's index was used to map the spatial autocorrelation (cluster) to showcase the spreading patterns of the COVID-19 pandemic in Malaysia. The study results indicated significant changes in the COVID-19 hotspots over time. At the beginning of 2020, the state of Selangor and Sarawak were the first locality to become a significant COVID-19 hotspot. Furthermore, this research showed all affected areas during the study period. Overall, a non-random distribution of COVID-19 occurrences was detected, thus suggesting a positive spatial autocorrelation. Many parties are affected by the COVID-19 pandemic, especially those involved in healthcare provision, financial assistance allocation, and law enforcement. Other sectors such as the economy, education, and religion are also affected. Therefore, the findings from this study will provide useful information to all the related governmental and private agencies, as well as policymakers and researchers.

Starting 2020, the whole world faced covid-19 pandemic that was spreading across countries including Malaysia. Because of this pandemic, Malaysia government take immediate action to declare Movement Control order (MCO). This MCO gave seriously impact to almost sectors in Malaysia including economic, social and also education sector. We believed that, the students’ performance become decrease during MCO. Although classes were continued online, teaching and learning activities are believed less effective than face-to-face classes. This study was conducted to measure the secondary school students’ performance during MCO for Mathematics subject. There were 2 set of mathematics score data before and during MCO considered in this study. The independent T-test was conducted to test is there any significant difference of performance between gender for both data sets. Meanwhile, the paired T-test was conducted to evaluate the significant different of student performance before and during MCO. It was found that, there was significant different of students’ performance before and during MCO which, the performance during MCO become declined. One of the possible factor that was gave significant effect to the student’ performance was lack of effectiveness teaching and learning activities using online platform. So, for future research, a study should be done in order to indicate the significant factor that give effect to students’ performance if teaching and learning activities carried out online.

This paper presents linear programming problems with all the parameters are fuzzy. Two approaches of membership functions are proposed. The first approach includes linear functions while the second includes linear and non linear functions. An example is given to illustrate the solution. The result shows that, the second approach is a better way to find the optimal solution.

The Covid-19 outbreak had a big effect on the global economy, but there was no exception for education. While this pandemic issue may seem like a force for change, it is also seen as a catalyst for the change of learning style patterns in line with new technology in the world of education. Due to the necessity to transition from face-to-face learning to online learning, the teaching and learning process at the school level will continue. Students have been exposed to online learning as a supplement to more traditional means of instruction, especially face-to-face instruction in the classroom, in order to maintain teaching and learning sessions. The use of digital technology in online learning has the potential to modernise and enrich the teaching and learning environment. The purpose of this study is to measure the participants’ performance in a short-term mathematics program by comparing the participants test scores before and after attending a short-term program known as “Program Bijak Sifir” during Movement Control Order, MCO. This study involves 44 kids (15 males and 29 females) from a variety of different primary schools. The Mann-Whitney test was used to determine whether there is a statistically significant difference in performance between genders and age group. Meanwhile, the Wilcoxon Signed Rank test was used to determine the significant difference in student performance before and after the training. There was a significant difference in participants’ performance prior to and during the program, with the latter showing an increase in performance. This demonstrates that effective use of digital technology in the classroom, even for short-term projects, has a favorable effect on student achievement.