Perbandingan Xgboost dan Logistic Regression dalam Memprediksi Credit Card Customer Churn

Rearizth Muhammad Daffaa; Deris Santika; Fathoni Mahardika

doi:10.61132/jupiter.v3i3.807

Authors

Rearizth Muhammad Daffaa Universitas Sebelas April
Deris Santika Universitas Sebelas April
Fathoni Mahardika Universitas Sebelas April

DOI:

https://doi.org/10.61132/jupiter.v3i3.807

Keywords:

Customer Churn, F1-Score, Logistic Regression, Precision, Recall

Abstract

With the times, cash transactions that used to use cash are now turning to credit cards. However, the increasing use of credit cards presents challenges, especially in maintaining customer loyalty. Customer churn is the loss of customers within a certain period for various reasons. Logistic Regression is a machine learning algorithm that studies the relationship between a dependent variable and several independent variables and Extreme Gradient Boosting (XGBoost) is a Gradient tree-boosting algorithm that offers out-of-core learning and sparsity awareness.The purpose of this study is to compare the performance between Logistic Regression and Extreme Gradient Boosting (XGBoost) algorithms in predicting customer churn in credit card services using evaluation metrics such as accuracy, precision, recall, and F1-score. Based on the research results, it can be concluded that XGBoost has better performance in all evaluation metrics, both in terms of precision, recall, F1-score, and accuracy. Based on the research, XGBoost shows superior performance compared to Logistic Regression in all evaluation metrics.

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