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.

This study aims to forecast Malaysian solid waste generation by identifying the state's landfill capacity to facilitate solid waste generated in the next two years. The solid waste management system depends extremely on landfill capacity. Due to the increased amount of solid waste generation, the authority is required to manage landfill utilization appropriately in selected regions, where landfill capacity was fully utilized. An accurate prediction of solid waste generation is required for the authority plan for landfill management. This paper provides the forecasting values for the seven states in Malaysia. The ARMA and ARIMA models are used to determine the best model for forecasting solid waste generation values. The results show that the ARIMA (2, 1, 1) model works best in Johor, Negeri Sembilan, and Wilayah Persekutuan Kuala Lumpur, while the ARIMA (1, 1, 2) model works best in Kedah and Perlis. Furthermore, the ARMA (1, 1) model is best for Pahang, and the ARMA (2, 1) model is best for Melaka. The ARIMA (3, 1, 1) model is the best for forecasting solid waste generation across all states. The findings are consistent with previous literature, which stated that solid waste generation would increase in one of Malaysia's districts over the next two years. They did not, however, consider the landfill's capacity to handle solid waste generation. These findings shed light on the potential volume of solid waste generated in the coming years, allowing authorized agencies to plan landfill capacity in Malaysia for environmental sustainability.

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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.

The economic dimension has revealed that weak economic growth is one of the causes of unemployment and is related to GDP. Forecasting the unemployment rate is essential and is an important determinant of monetary policy decisions and needs to be addressed. This study is conducted to identify whether novel coronavirus 2019, COVID-19 affects Malaysia's unemployment rate and forecast the rate for the next two years. The Box-Jenkins approach uses the Augmented Dickey-Fuller test to stabilize the data. After reducing the trend pattern of the partial autocorrelation coefficient, the ARIMA prediction method ARIMA (2,1,2) was selected as the best model to apply to the unemployment rate time series data. As a result, the projected unemployment rate graph showed a steady increase over the next two years. For future research, it is recommended to consider factors such as inflation, growth domestic product and employment to predict this value to improve results.