Penerapan Algoritma Machine Learning Untuk Memprediksi Term Deposit Nasabah Perbankan

Muhammad Rasikh Azfa Riyyasy; Wahyu Nouval Aghniya; Henri Tantyoko

Muhammad Rasikh Azfa Riyyasy Institut Teknologi Telkom Purwokerto
Wahyu Nouval Aghniya Institut Teknologi Telkom Purwokerto
Henri Tantyoko Institut Teknologi Telkom Purwokerto

Keywords: Customers, Deposits, Random Forest, Logistic Regression, SVC, XGBoost

Abstract

The banking sector has a very important influence on the economic conditions of a country which makes banks continue to innovate in each of their products. One of the things offered by banks is through their deposit products. Deposits are savings from which withdrawals can only be made at certain times based on the depositor's agreement with the bank. Over time, banks have launched various types and forms of investment as new products which have an impact on customers switching to new investment products. The aim of this research is to determine the deposit terms of banking customers. The methods used are Random Forest, Logistic Regression, SVC, and XGBoost. The research results show that the best models are the random forest and xgboost models with an accuracy of 91% without any overfitting.