Application of SMOTE to Handle Imbalance Class in Deposit Classification Using the Extreme Gradient Boosting Algorithm - Dina Arifah; Triando Hamonangan Saragih; Dwi Kartini; Muliadi Muliadi; Muhammad Itqan Mazdadi (Universitas Ahmad Dahlan, 2023)
Deposits became one of the main products and funding sources for banks and increasing deposit marketing is very important. However, telemarketing as a form of deposit marketing is less effective and efficient as it requires calling every customer for deposit offers. Therefore, the identification of potential deposit customers was necessary so that telemarketing became more effective and efficient by targeting the right customers, thus improving bank marketing performance with the ultimate goal of increasing sources of funding for banks. To identify customers, data mining is used with the UCI Bank Marketing Dataset from a Portuguese banking institution. This dataset consists of 45,211 records with 17 attributes. The classification algorithm used is Extreme Gradient Boosting (XGBoost) which is suitable for large data. The data used has a high-class imbalance, with