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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Dengue fever is a disease caused by one of the four dengue viruses and this disease is an infectious disease that is spread through the bite of the Aedes Aegypti mosquito. When compared with the number of dengue cases in previous years, East Nusa Tenggara (NTT) was one of the provinces that experienced an increase in the number of dengue cases in the last three years. Previous research states that the transmission of dengue fever is caused by several factors, one of which is environmental factors of geographical location so that spatial aspects need to be involved in this study. A the statistical method that can be used to analyze spatial data in the form of a logistic regression equation that has a binary response variable is the Geographically Weighted Logistic Regression (GWLR) method. This study aims to analyze the factors that influence the high number of dengue fever cases in NTT in 2018 using GWLR approach by weighted the Gaussian kernel function. Based on the results of GWLR analysis, the number of rainy days and the number of health workers partially significantly influence the status of dengue fever events in each regency/city in NTT Province in 2018. Based on the calculation of Press’s Q value, the prediction in this study was accurate with the accuracy of classification was 0.8636 or 86.36%.