DOI of the published article https://doi.org/10.47065/bits.v4i4.3212
Fraud Classification on Bank Accounts using Ensemble Learning Approach
Klasifikasi Penipuan pada Rekening Bank menggunakan Pendekatan Ensemble Learning
DOI:
https://doi.org/10.21070/ups.733Keywords:
Bank Account, Extreme Gradiant Boosting, Fraud, Classification, Random ForestAbstract
Accounts are a collection of numbers commonly used for all transactions in the banking world. In the process of opening it there are attempts at criminal acts. In an effort to prevent of fraud can be solved using data mining techniques, namely classification. Therefore, this research is made to classify bank accounts for fraud prevention. The detection results are classified into two classes, namely, prospective customers indicated fraud and prospective customers not indicated fraud. The classification methods used in this study are XGBoost and random forest with parameter tuning processed by randomized search cross validation.This study obtained a train score of 99.50% and a test score of 99.59% for XGBoost while Random Forest obtained a train score of 99.46% and a test score of 99.59%. These results show that the classification results of XGBoost are better than random forest.
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