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.
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.
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.
The purpose of this research is to determine the unwanted condition as a strategic criterion in measuring the economic resilience of a city. A new approach in determining economic resilience was developed to overcome the weaknesses of the index method commonly used internationally. Based on the output of this research, the development priority program for each city becomes distinctive depending on the status of the city’s economic resilience. Quality improvement programs are used for cities that do not have resilience and retention programs for cities that already have economic resilience. Five piecewise linear regression parameters are applied to identify a statistical model between Income per capita and Pc as a concern variable and modifier variable, and a Z. Model is tested massively involving all 514 cities in Indonesia from 2015 to 2019, covering the components of the modifier variable: local revenue (PAD), poverty, unemployment and concern variable; GRDP and population. The value of the Fraction of variance unexplained (FVU) of the model is 40%. This value is obtained using the Rosenbrock Pattern Search estimation method with a maximum number of iterations of 200 and a convergence criterion of 0.0001. The FVU area is a condition of uncertainty and unpredictability, so that people will avoid this area. This condition is chaotic and declared as an unwanted condition. The chaotic area is located in the value of UZ less than IDR 5,097,592 and Pc < Pc (UZ) = 27,816,310.68, and thus the coordinates of the chaotic boundary area is (5,097,592: 27,816,310.68). FVU as a chaotic area is used as the basis for stating whether or not a city falls into unwanted conditions. A city is claimed not to be economically resilient if the modifier variable Z is in a chaotic boundary.
Today, the economic resilience in Indonesia measures using the index approach, but it does not consider the effect of the disturbance and causes meaningless. The index is essentially an average, and the average is not a model that captures the relationship between variables. This research differs significantly from earlier studies that used the index to measure economic resilience. The crucial step in assessing the economic resilience of a city is to determine the economic resilience decision variable itself. If a variable significantly correlates with the disturbance factors in each relationship pattern, it is considered suitable as an economic resilience variable. This study evaluates variable Z as an economic resilience variable with a significant relationship to its disturbance variable. The evaluation method is conducted in-depth by studying Indonesia's cities over five years (2015-2019). Z, the ratio of Original Local Government Revenue (PAD) to the number of poor people in a city as a cost centre, will be evaluated as a prospective decision variable for economic resilience. The statistical relationship between Z and 9 disturbance variables is examined using piecewise linear regression analysis. All 514 cities in Indonesia were observed extensively for identification during a five-year observation period. Rosenbrock pattern search estimation was used to estimate the model parameters. The following results were obtained by analysing the data with the STATISTICA software. As determined by parsimonious analysis, the price of Pertalite fuel and the US dollar foreign exchange are two disturbance factors that are crucial to the fall in the resilience variable Z. The joint effect of these two variables on the decline in the resilience measure Z is 73.63 percent. The study concludes that Z is a city-level economic resilience decision variable that applies to all 514 cities in Indonesia and is measured as the ratio of PAD to the number of poor people. This study's novel contribution to Indonesian policymakers is Z as a new economic resilience decision variable that can be used to assess cities' relative economic resilience.
Infant mortality rate is one of several other indicators that are very susceptible in order to illustrate the mark of public health problems. This indicator is substantial to know the cause of the deaths as well as how success the maternal and child health program. This study aims to determine the characteristics of the causes of infant deaths based on age groups and sub-districts in the city of Bandung using multiple correspondence analysis. The results of the study concluded that asphyxia, early neonatal age groups provide significant positive dependence on the sub-district of Bojongloa Kaler, Bojongloa Kidul, Cicendo, Cidadap, Lengkong. Asphyxia, infant age groups provide significant positive dependence on sub-district of Cibeunying Kidul, Cinambo, Kiara Condong. Low birth weight, early neonatal age groups provide significant positive dependence on sub-district of Andir, Rancasari. Congenital abnormalities, early neonatal age groups provide significant positive dependence on sub-district of Babakan Ciparay, Batununggal, Sukajadi. Congenital abnormalities, advanced neonatal age groups provide significant positive dependence on sub-district of Bandung Kidul. Congenital abnormalities and infant age groups provide a significant positive dependence on sub-district of Coblong. Pneumonia, the early neonatal age group provide a significant positive dependence on the sub-district of Bandung Kulon. Sepsis, infant age groups provide significant positive dependence on sub-district of Bandung Wetan.
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