KLASIFIKASI TRANSAKSI PENIPUAN PADA KARTU KREDIT MENGGUNAKAN METODE RESAMPLING DAN PEMBELAJARAN MESIN

FEBRIADY, MUKHLIS and Samsuryadi, Samsuryadi and Rini, Dian Palupi (2021) KLASIFIKASI TRANSAKSI PENIPUAN PADA KARTU KREDIT MENGGUNAKAN METODE RESAMPLING DAN PEMBELAJARAN MESIN. Master thesis, Sriwijaya University.

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Abstract

The high number of credit card fraud causes a lot of losses for both users and credit card service providers. Because the rate of credit card transactions is very fast, it is necessary to detect credit card fraud as early as possible. However, another challenge that is no less important is the unbalanced amount of data (balance data) between valid and invalid transactions. One solution to the problem of data imbalance is to use a resampling method that can improve the quantity of data so that the accuracy results are better. In this study, three types of resampling methods were applied, namely SMOTE, bootstrap, and Jackknife. Furthermore, to validate the success of the resampling method, three types of machine learning methods were used, namely SVM, ANN, and Random Forest. The test results show that the combination of SMOTE and Random Forest resampling methods produces the best performance with accuracy, precision, recall and F1-score values of 99,95%, 81,63%, 90,91% and 86,02%, respectively. Keywords: imbalance data, resampling method, credit card fraud, machine learning, credit card.

Item Type:	Thesis (Master)
Uncontrolled Keywords:	Imbalance data, metode resampling, penipuan kartu kredit, pembelajaran mesin, kartu kredit
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning
Divisions:	09-Faculty of Computer Science > 55101-Informatics (S2)
Depositing User:	Mr Mukhlis Febriady
Date Deposited:	04 Apr 2022 04:43
Last Modified:	04 Apr 2022 04:43
URI:	http://repository.unsri.ac.id/id/eprint/67544

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