Central Java province is one of the provinces with the highest number of poor people on the island of Java, with the number of poor people in 2020 increasing by 0.44 million people from the previous year. Poverty is caused by several factors, one of which is the Human Development Index (HDI) and the Total Population level. Each region has different characteristics from other regions. These differences in characteristics cause more specific spatial effects, namely spatial heterogeneity. Geographically Weighted Regression (GWR) is a statistical method that can analyze spatial heterogeneity by assigning different weights and models to each observation location. This study aims to determine whether the HDI variable and percentage of total population significantly impact the number of poor people in Central Java Province in 2020 without eliminating the spatial effect. There are three groupings of variables that affect the Number of Poor People for GWR with the Adaptive Kernel Bisquare weighting function and four groups for the Adaptive Kernel Tricube weighting function. The Key Performance Indicators (KPI) used are Mean , Akaike Information Criterion (AIC), Absolute Error (MAE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE). Based on these KPIs, the GWR model with the Adaptive Kernel Bisquare weighting function provides better results when compared to the OLS model.

Indonesia is the largest archipelagic country in the world (based on area and population), which makes it as one of countries with the most significant maritime activities. Therefore, there has been a high rate of maritime accidents in Indonesia. The National Search and Rescue Agency (BASARNAS) as a non-ministerial government agency with the primary task of Search and Rescue (SAR) operation deals with several types of accidents, including maritime accidents. Response time as the time to receive news about the accidents until the SAR unit comes to the rescue is very crucial in this matter. Average response time is stipulated based on BASARNAS’s regulations to estimate information about the survival probability of the victims. This research concerns with the survival analysis using Kaplan-Meier Method and Log-Rank Test. The researchers categorized maritime accidents into three categories: ‘Low’, ‘Medium’, and ‘High’. This classification aims to find out whether the survival function of each category has the same or different function and to investigate whether there are differences from the given responses or not. The survival analysis with Kaplan-Meier method revealed that the three categories had different survival functions. The survival analysis was followed by a Log-Rank Test. The final result shows that there is no difference in the responses given by the three categories when maritime accidents occur. Received February 10, 2021Revised March 29, 2021Accepted March 29, 2021

Central Java province is one of the provinces with the highest number of poor people on the island of Java, with the number of poor people in 2020 increasing by 0.44 million people from the previous year. Poverty is caused by several factors, one of which is the Human Development Index (HDI) and the Total Population level. Each region has different characteristics from other regions. These differences in characteristics cause more specific spatial effects, namely spatial heterogeneity. Geographically Weighted Regression (GWR) is a statistical method that can analyze spatial heterogeneity by assigning different weights and models to each observation location. This study aims to determine whether the HDI variable and percentage of total population significantly impact the number of poor people in Central Java Province in 2020 without eliminating the spatial effect. There are three groupings of variables that affect the Number of Poor People for GWR with the Adaptive Kernel Bisquare weighting function and four groups for the Adaptive Kernel Tricube weighting function. The Key Performance Indicators (KPI) used are Mean , Akaike Information Criterion (AIC), Absolute Error (MAE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE). Based on these KPIs, the GWR model with the Adaptive Kernel Bisquare weighting function provides better results when compared to the OLS model.