At present, the intrusion detection system is the most developed trend in society. The intrusion detection system acts as a defense tool to detect security attacks which has been increasing steadily in recent years. Therefore, global information security is a very serious problem. As the types of attacks that emerge are constantly changing, there is a need to develop adaptive and flexible security features. Selection feature is one of the focuses of research on data mining for datasets that have relatively many attributes. In this study, the author tries to analyze the NSL-KDD data set with the selected attributes classified in two ways, namely binary classification (attack or not attack) and five classification classes using multinomial logistics, namely Dos, R2L, U2R, Probe and Normal. The results showed that the NSL-KDD dataset for the classification of attacks on the Intrusion Detection System (IDS) using binary logistics can increase the classification accuracy to 92.3% and 91.7% for datasets with multinomial logistics.

The Geographically Weighted Logistic Regression (GWLR) model is a logistic regression model development that is applied to spatial data from non-stationary processes. This model is used to predict a model of the data set that has a binary response variable which takes into account the spatial factor. This study will discuss the use of the GWLR model using the adaptive weighting function of the Gaussian kernel in a poverty case study in East Nusa Tenggara Province in 2019.The parameter estimation of the Maximum Likelihood Estimation (MLE) method by giving different weights for each observation location. The weight used is the adaptive Gaussian kernel with the optimum bandwidth selection using the Cross-Validation (CV). Based on the results of testing the parameters of the GWLR model with a weighted adaptive Gaussian kernel, it can be concluded that the factors that influence poverty are local and vary in the 22 observation locations, including GRDP per capita, acceptance of smart Indonesian programs, and projected population growth rates, with a classification accuracy rate of 81,82%.