No abstract

COVID-19 is rapidly expanding across the globe. As a Southeast Asian region, Malaysia has also been affected by COVID-19. Since the COVID-19 outbreak first emerged in China at the end of 2019, Malaysia has taken precautionary measures to prevent entering the nation. However, since COVID-19 is more than undoubtedly unstoppable, Malaysia eventually received the first case in early January 2020. The increase in the epidemic scale has led to the (preface of non-pharmaceutical countermeasures). Hence, it is of utmost importance to analyze the trends of the cases to develop a forecasting model that could anticipate the number of confirmed COVID-19 cases in Malaysia and select the best forecasting model based on forecast measure accuracy to forecast the future course of outcomes. For this purpose, the number of daily cases from 15 March 2020 to 31 March 2021 was retrieved from the Ministry of Health (MOH) website and estimated using the Box-Jenkins approach. There were five models developed such ARIMA (1,1,1), ARIMA (1,1,2), ARIMA (1,1,3), ARIMA (2,1,1) and ARIMA (2,1,2). The models' effectiveness is evaluated based on AIC, BIC and RMSE criteria. The findings indicate that ARIMA (1,1,3) is the preferred model for forecasting since it has better performance regarding adopted criteria than compared models. The forecasted values showed an upward trend of COVID-19 cases until January 2022. In conclusion, subsequent studies would yield more discoveries and a more systematic approach to have better and more accurate forecasting. In the instance of the COVID-19, the recommended model appears to be correct. More complex modelling methodologies and extensive information on the disease are required to forecast the pandemic.

No abstract

This paper describes modeling and simulation of reverse logistics networks for collection of used computers in one of the company in Selangor. The study focuses on design of reverse logistics network for used computers recycling operation. Simulation modeling, presented in this work allows the user to analyze the future performance of the network and to understand the complex relationship between the parties involved. The findings from the simulation suggest that the model calculates processing time and resource utilization in a predictable manner. In this study, the simulation model was developed by using Arena simulation package.

The primary aim of this study is to determine which factors have more contributions to the achievement of productivity of rubber industry in Selangor. This study is expected to provide significant information necessary for Rubber Industry Smallholders' Development Authority (RISDA) to plan strategies to increase the number of smallholders to achieve the target productivity. The independent variables are the age of rubber trees, the number of rubber trees tapped per year, the type of clone used, the type of tapping system, and the usage of stimulation. Logistic regression would be undertaken in analyzing the productivity achievement of rubber tappers. Additionally, the misclassification rate had been used as the criterion for judging the efficacy of a classifier of productivity and also sensitivity and specificity were statistical measure of the performance of binary classification. Based on the Wald test; land area, age of rubber trees, rubber land area, and usage of stimulation contribute significantly to the model. Logistic regression model fits the data based on the Hosmer and Lemeshow Goodness of Fit Test. Overall, 91.3% of the cases are classified correctly and this model posses good predictive efficiency. The results of the study indicate that the factors influence the achieving the target productivity were identified and therefore appropriate actions can be taken to promote and enable the smallholders to achieve the target productivity.