Detection of Credit Card Fraud with Machine Learning Methods and Resampling Techniques
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 classi...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2022-12-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | http://jurnal.iaii.or.id/index.php/RESTI/article/view/4213 |
| _version_ | 1827361612574490624 |
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| author | Moh. Badris Sholeh Rahmatullah Aulia Ligar Salma Hanani Akmal Muhammad Naim Zamah Sari Yufis Azhar |
| author_facet | Moh. Badris Sholeh Rahmatullah Aulia Ligar Salma Hanani Akmal Muhammad Naim Zamah Sari Yufis Azhar |
| author_sort | Moh. Badris Sholeh Rahmatullah |
| collection | DOAJ |
| description | 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 "Credit Card Fraud Detection" with a total of 284807 data which is divided into two classes: Not Fraud and Fraud. The proposed model received a recall of 92% with Gradient Boosting, where the results increased by 10.37% compared to the previous study using Random Forest with a recall result of 81.63%. This is because the use of SMOTE in the data train greatly influences the classification of Not fraud and fraud classes. |
| first_indexed | 2024-03-08T07:08:10Z |
| format | Article |
| id | doaj.art-e40375065a2f419191abd57485d4f2bd |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T07:08:10Z |
| publishDate | 2022-12-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-e40375065a2f419191abd57485d4f2bd2024-02-03T04:01:52ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602022-12-016692392910.29207/resti.v6i6.42134213Detection of Credit Card Fraud with Machine Learning Methods and Resampling TechniquesMoh. Badris Sholeh Rahmatullah0Aulia Ligar Salma Hanani1Akmal Muhammad Naim2Zamah Sari3Yufis Azhar4Universitas Muhammadiyah MalangUniversitas Muhammadiyah MalangUniversitas Muhammadiyah MalangUniversitas Muhammadiyah MalangUniversitas Muhammadiyah MalangFinancial 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 "Credit Card Fraud Detection" with a total of 284807 data which is divided into two classes: Not Fraud and Fraud. The proposed model received a recall of 92% with Gradient Boosting, where the results increased by 10.37% compared to the previous study using Random Forest with a recall result of 81.63%. This is because the use of SMOTE in the data train greatly influences the classification of Not fraud and fraud classes.http://jurnal.iaii.or.id/index.php/RESTI/article/view/4213machine learningensemble learningclassificationresamplingcredit card fraud |
| spellingShingle | Moh. Badris Sholeh Rahmatullah Aulia Ligar Salma Hanani Akmal Muhammad Naim Zamah Sari Yufis Azhar Detection of Credit Card Fraud with Machine Learning Methods and Resampling Techniques Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) machine learning ensemble learning classification resampling credit card fraud |
| title | Detection of Credit Card Fraud with Machine Learning Methods and Resampling Techniques |
| title_full | Detection of Credit Card Fraud with Machine Learning Methods and Resampling Techniques |
| title_fullStr | Detection of Credit Card Fraud with Machine Learning Methods and Resampling Techniques |
| title_full_unstemmed | Detection of Credit Card Fraud with Machine Learning Methods and Resampling Techniques |
| title_short | Detection of Credit Card Fraud with Machine Learning Methods and Resampling Techniques |
| title_sort | detection of credit card fraud with machine learning methods and resampling techniques |
| topic | machine learning ensemble learning classification resampling credit card fraud |
| url | http://jurnal.iaii.or.id/index.php/RESTI/article/view/4213 |
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