Identifying Credit Card Fraud in Illegal Transactions Using Random Forest and Decision Tree Algorithms

Indah Werdiningsih; Endah Purwanti; Gede Rangga Wira Aditya; Auliya Rakhman Hidayat; R. Sulthan Rafi Athallah; Virda Adisty Sahar; Tio Satrio Wibisono; Darren Febriand Nura Somba

doi:10.32736/sisfokom.v12i3.1730

Authors

Indah Werdiningsih Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Airlangga
Endah Purwanti Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Airlangga
Gede Rangga Wira Aditya Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Airlangga
Auliya Rakhman Hidayat Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Airlangga
R. Sulthan Rafi Athallah Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Airlangga
Virda Adisty Sahar Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Airlangga
Tio Satrio Wibisono Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Airlangga
Darren Febriand Nura Somba Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Airlangga

DOI:

https://doi.org/10.32736/sisfokom.v12i3.1730

Keywords:

Credit Card, Classification, Decision Tree, Random Forest

Abstract

The use of credit cards is increasing in today's digital era. This increase has resulted in many cases of fraud which have had a negative impact on credit card owners. To overcome this, many financial institutions have developed credit card fraud detection systems that can identify suspicious transactions. This study uses a classification method, namely random forest and decision tree to identify illegal transactions using a credit card, which then compares the results and attempts to create a model that can be useful for detecting fraud using a credit card that is more accurate and effective. The result of this study is that the accuracy provided by the Decision Tree Classifier is 0.98, while the accuracy provided by the Random Forest Classification is also 0.975. The conclusion obtained that the decision tree has a higher level of accuracy compared to the Random Forest Classification Algorithm, which is 98%. On the other hand, the Random Forest classification algorithm has a slightly lower level of accuracy compared to the Decision Tree classification algorithm, with an accuracy rate of 97.5%

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