One of the biggest problems in the continuity of one’s education is the education fee which is often unaffordable. Therefore, the existence of education insurance is a solution to this problem. Along with increasing public interest in education insurance, insurance companies need to adjust the claims reserves with the number of claims paid to maintain the company’s capital. Claim reserves are funds that must be provided by insurance companies to fulfil obligations to policy holders in the future. Losses and inaccuracies in the payment of insurance claims will result in the policy holder and the insurance company itself. Therefore, it is necessary to do a prediction of insurance company’s monthly reserve claims. In education insurance, the claim reserve data has seasonal characteristics and the number of educational insurance claims tends to increase at the turn of the school year. These fluctuating patterns are supposed to fit the application of the SARIMA model and the nonparametric regression model with the Fourier series estimator in forecasting. Fourier series is a function that has flexibility in approaching fluctuating, seasonal, and recurring data patterns. The results showed that the prediction accuracy of the SARIMAX model was higher than the nonparametric regression model with MAPE of 15% and 4% respectively.

This study describes a new idea about comparing two new estimations in nonparametric regression for multiresponse cases or simultaneously model based on Fourier series and kernel estimators enabling the prediction of Indonesian strategic commodity prices during the COVID-19 pandemic. Based on the National Strategic Food Price Information Center in Indonesia, there are 10 strategic commodities in the agriculture, livestock, fishery, and horticultural sectors, which have had the biggest endowment to secure food supplies and the formation of inflation figures in Indonesia. These commodities include rice, chicken meat, beef, chicken, egg, onion, garlic, chili, cayenne, cooking oil, and sugar. Using the goodness estimator of criteria in nonparametric regression, such as the smaller Generalized Cross-Validation, the smaller Mean Square Error, and the larger determination coefficient (R2), the result of this study is Fourier series estimator to predict the prices of 10 food commodities, simultaneously. When compared with the kernel estimator, the Fourier series estimator meets the criteria of goodness with a smaller Mean Square Error value of 0.052 and a larger determination coefficient of 99.0472%. The selected estimator has very good performance to predict the prices of 10 food commodities, because the prediction result has very small Mean Absolute Percentage Error equaling 0.0443%. This prediction result can be used for the government to monitor and evaluate price fluctuations for 10 commodities so that the stability of national strategic commodities becomes daily consumption to be maintained, especially during the COVID-19 pandemic.

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This article describes a new idea called AMiBI. It is a mitigation platform based on the fact that flood still becomes an annual problem in Indonesia. According to the National Disaster Management Agency or Badan Nasional Penanggulangan Bencana (BNPB), 649 flood incidents occurred from 2011 until 2019 in Indonesia. Natural factors are certainly not the only main factors causing it. Environmental damage plays a more crucial role. One of the causes of this damage is the existence of settlements along the riverbanks. This factor exactly should be controlled by humans. Still, economic needs have become the main reason driving people to survive in big cities by establishing illegal settlements along riverbanks. Regarding these facts, AMiBi was also built under statistical analysis by modeling the flood incidents based on the number of settlements along riverbanks using the local linear nonparametric regression. Its result shows that the model has R2 value of 51.48% and a Mean Square Error (MSE) of 24.26. It also performs a linear relationship between those variables, which means that the existence of settlements along riverbanks significantly affects the number of flood incidents. Regarding those analyses as the basis of development, this digital platform performs several services for reducing loss potency caused and supporting the awareness to build a sustainable environment in riverbanks. Considering AMiBI as the only platform that uses statistical modeling as the basis of services and implementation, it has a significant role in supporting Indonesia as a smart country for mitigation.

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