COVID-19 has spread to more than a hundred countries worldwide since the first case reported in late 2019 in Wuhan, China. As one of the countries affected by the spread of COVID-19 cases, the local government of Malaysia has issued several policies to reduce the spread of this outbreak. One of the measures taken by the Malaysian government, namely the Movement Control Order, has been carried out since March 18, 2020. In order to provide precise information to the government so that it can take the appropriate measures, many researchers have attempted to predict and create the model for these cases to identify the number of cases each day and the peak of this pandemic. Therefore, hospitals and health workers can anticipate a surge in COVID-19 patients. In this research, confirmed, recovered, and death cases prediction was performed using the neural network as one of the machine learning methods with high accuracy. The neural network model used is the Multi-Layer Perceptron, Neural Network Auto-Regressive, and Extreme Learning Machine. The three models calculated the average percentage error (APE) values for 7 days and obtained APE values for most cases less than 10%; only 1 case in the last day of one method had an APE value of approximately 11%. Furthermore, based on the best model, then the forecast is made for the next 7 days. In conclusion, this study identified that the MLP model is the best model for 7-step ahead forecasting for confirmed, recovered, and death cases in Malaysia. However, according to the result of testing data, the ELM performs better than the MLP model.

Recent research uses an index to measure economic resilience, but the index is inadequate because it is impossible to determine which disturbance factors have the greatest impact on the economic resilience of cities. This study aims to develop a new methodology to measure the economic resilience of a city by simultaneously examining unwanted conditions and disturbance factors. The ratio of regional original income to the number of poor people is known as Z and is identified as a measure of economic resilience in Indonesia. Resilience is measured by Z’s position in relation to the unwanted area following a specific level of disturbance. If Z is in the unwanted condition, the city’s per capita income will decrease, and the city will be considered economically not resilient. The results of the analysis show that six levels of economic resilience have been successfully distinguished based on research on 514 cities in Indonesia involving nine indicators of disturbance and one variable of economic resilience during the five-year observation period, 2015–2019. Only 3.11 percent of cities have economic resilience level 1, while 69.18 percent have level 0. Economically resilient cities consist of 4.24 percent of cities at level 2, as much as 3.39 percent at level 3, as much as 3.39 percent at level 4, and as much as 16.69 percent at level 5. The novelty of this research is to provide a new methodology for measuring the economic resilience of cities by integrating unwanted conditions as necessary conditions and disturbance factors as sufficient conditions. The measurement of a city’s economic resilience is critical to help the city government assess the security of the city so the government can take preventive actions to avoid the cities falling into unwanted conditions.

The progress of development based on economic indicators, is considered not reflect the level of welfare. Happiness Index is an indicator that measures of well-being subjectively is beyond GDP. Happiness Index is a composite index based on the level of satisfaction with the 10 essential aspects of life: health, education, occupation, household income, family harmony, the availability of free time, social relations, housing conditions and assets, the environment and safety conditions. Bandung Regional Development Planning Agency has cooperated with the Laboratory of Quality Control Department of Statistics University Padjadjaran to measure the level of happiness of the population of Bandung. Survey carried out in 30 Districts with the random sampling design that is intended to represent the level of happiness of the citizens of Bandung. Covered 151 villages in Bandung with a sample of more than 2 times than in 2014- SPTK-BPS Happiness index of Bandung in 2015 was 70.60. Calculations using the framework of the American Customer Satisfaction Index produces greater happiness index which is 74. Three aspects of life that have the highest contribution are Employment (11.91%), Social Affairs (11:39%) and Harmony Family (11.28%). The highest happiness index is related to family harmony. Strategic recommendations given are the increase in the program: employment and self-employment, housing, education, increased hedonic level of Affect, increase self-function.

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In this paper, we determined the factors that affect the waiting time of rice farmers’ willingness to pay the premium for the Rice Farming Insurance Program (RFIP) using survival analysis. The survival analysis method was carried out using the Cox proportional hazard model with the Efron approach. The case study in this research is rice farmers in Cibungur Village, Parungponteng District, Tasikmalaya Regency. The results of the analysis show that the predictor variables that are significant to the waiting time of rice farmers’ willingness to pay the insurance premium for RFIP are their last education, other occupations, rice production, and farming costs. The results of the research are expected to produce additional information for the government and implementers of rice farming insurance regarding the condition of farmers in the field, so that it can be improved in the future.