Detection of Credit Card Fraud with Machine Learning Methods and Resampling Techniques - Moh. Badris Sholeh Rahmatullah; Aulia Ligar Salma Hanani; Akmal Muhammad Naim; Zamah Sari; Yufis Azhar (Ikatan Ahli Informatika Indonesia (IAII), 2022)
Financial institutions in the form of banks provide facilities in the form of credit cards, but with the development of technology, fraud on credit card transactions is still common, so a system is needed that can detect fraud transactions quickly and accurately. Therefore, this study aims to classify fraudulent transactions. The proposed method is Ensemble Learning which will be tested using the Boosting type with 3 variations, namely XGBoost, Gradient Boosting, and AdaBoost. Then, to maximize the performance of the model, the dataset used is optimized with the Synthetic Minority Oversampling Technique (SMOTE) function from the Imblearn library in the data train to handle imbalanced dataset conditions. The dataset used in this study is entitled