COVID-19 is a contagious disease caused by the coronavirus (SARS-CoV-2) that attacks the respiratory tract. On August 14th, 2021, 653,741 persons had been proven positive for COVID-19. The number of patients tends to increase as the number of COVID-19 cases grows. The more infected people, the more cases of COVID-19 there will be. The Bed Occupancy Ratio (BOR) in West Java reached an all-time high of 91.6 percent in June 2021, far exceeding the WHO recommendation of 60 percent, before gradually declining to 30.69 percent in August. Because of the new cases mentioned, the rate of spread of COVID-19 in West Java, the forecast of new cases is very strategic. The number of new cases in this study was predicted using a Multilayer Perceptron (MLP). The data used in this study were sourced from the COVID-19 Task Force. The data is the number of positive and new cases from 34 provinces in Indonesia from March 2nd, 2020, to August 14th, 2021. The results of the evaluation using test data on the number of active cases in the last 19 weeks, namely April 10th - August 14th, 2021, The MLP is accurate in forecasting the number of new cases 18 times for both forecast periods with APE < 15%, with the value MAPE, RMSE and MAE obtained were 5.52%, 1157,61, and 706.811. The results of this study can be helpful for the government as a reference in conditioning hospital bed capacity to deal with active COVID-19 cases in West Java in the next two weeks so that the hospital rejects no COVID-19 patients because the hospital is full.

As the most contributed sectors in agriculture, rice farming is facing various risks, namely uncertainty such as crop failure caused by climate change, including air temperature, weather, rainfall and others. Indonesia is categorised as an agricultural country with a tropical climate. By this season, the farmers can plant the rice. Rice farming is currently an inseparable part of most agricultural societies in Indonesia, especially in West Java. However, changes in air temperature, weather and annual rainfall, can increase the uncertainty and upward the risk of crop failure. Thus, the current study seeks to investigate the decision making for agricultural risk assessment (climate variable) through the formulation of a risk model for agricultural insurance in Indonesia. This study utilised the climate variables, which consist of air temperature, wind speed, maximum and minimum temperatures, and rainfall. For determining the magnitude of risk, we applied the Block Maxima method and Peak Over Threshold. The results of this study found that the highest risk of losses occurred in November, December, January, February and March with a value of 0.17485.

As a country surrounded by the ocean, Indonesia is categorized as a country that has marine potential. The fishermen communities’ economy depends on ocean. However, the fishermen communities live below the poverty line and their average income is less than regional minimum wage. In conjunction with the issue, this study seeks to investigate the factors affecting the income of fishermen communities and right decision to fishermen in covering with welfare insurance in Cirebon, Indonesia. The quantitative study is designed using cross-sectional approach. The data collected by applying random sampling with open-ended questions and interview. A total of 100 fishermen’s have participated in this study. The study used some factors in measuring the fishermen community income, namely coastal environment condition, fish catching technology and location, operational capital, climate (season) condition, fishermen’s age, fishermen’s education, and fishing experience. The data are analyzed using the multinomial logistic regression model by assisting the statistical software, i.e., SPSS-23. The results show that coastal environment condition, fish catching technology and location, operational capital, climate (season) condition, fishermen’s age, fishermen’s education, and fishing experience have significant effects on fishermen income. Interestingly, the factor of coastal environment condition and climate (season) condition have significant negative effects on fishermen income. In conclusion, this study identified that two important factors reduced the welfare level of fishermen (via income). Also, in line with that things, the right decision which can provide to support and assist the fishermen community was by providing the welfare insurance. It is purposely to give them the protection from various risks faced by fishermen.

The agricultural sector is directly affected by climate variables. The presence of climate variability causes a considerable risk to agricultural productivities. Thus, risk management is an alternative to reduce risks, including optimizing the allocation of farmland and choosing crop insurance for a specific planting date. The purpose of this study is to investigate the agricultural risk management through risk measure of climate variability using the Conditional Value-at-Risk (CVaR) in rice production. This paper investigated several possible considerations of agricultural insurance premiums based on losses climate index. We concluded that the climate index insurance policy is the best choice that farmers can choose for each planting date, the higher the significance value considered, the more the value of Value-at-Risk and Conditional Value-at-Risk.