Ensemble Bagging Discriminant and Logistic Regression in Classification Analysis

Abstract

The main problem that often becomes a challenge in classification analysis is a class imbalance, for example, in bank credit collectability where performing loans (PL) are 5% and non-performing loans (NPL) are 95%. The purpose of this research is to develop a classification model for the imbalance of collectibility data on Bank X mortgage credit. The analysis developed is Ensemble on Discriminant Analysis and Logistic Regression. The ensemble used in this study is Bagging (Bootstrap Aggregating). The data used are secondary data on bank X mortgage credit collectibility with a sample of [Formula: see text] = 100 and simulation data. Generated data with [Formula: see text] = 1000 and consists of two scenarios, namely, data with unbalanced classes (50:950) and data with balanced classes (500:500). Evaluation of the classification model is seen from the accuracy, sensitivity, and specificity. The results of classification analysis with Ensemble using Bagging Discriminant and Logistic Regression Bagging on secondary data and simulations are better than ordinary Discriminant Analysis and Logistic Regression. The sensitivity and specificity of the credit collectibility classification using Bagging Discriminant and Logistic Regression Bagging are also higher. The originality in this study is in the form of an Ensemble model using Bagging Discriminant and Logistic Regression which can improve the performance of classification analysis and has implications for reducing risk both for banks and for Bank X KPR customers.