Implementation of Classification Algorithm for Anomaly Detection in Credit Card Transactions with RapidMiner

Fitria; Emy Iryanie; Heldalina; Heru Kartika Chandra

doi:10.59890/ijist.v3i7.126

Authors

Fitria Politeknik Negeri Banjarmasin
Emy Iryanie Politeknik Negeri Banjarmasin
Heldalina Politeknik Negeri Banjarmasin
Heru Kartika Chandra Universitas Negeri Banjarmasin

DOI:

https://doi.org/10.59890/ijist.v3i7.126

Keywords:

Random Forest, Credit Card, Anomaly Detection, Feature Engineering

Abstract

This study evaluates the application of the Random Forest algorithm in the classification of credit card transactions to detect transaction types such as cash_out, cash_in, payment, transfer, and debit. The dataset used is derived from Kaggle.com and includes attributes such as the number of transactions, sender and recipient balances, and transaction types. The results showed an accuracy of 80.63%, with the best performance in cash_out and payments, but difficulties in classifying debit and transfer transactions due to class imbalances. Classroom balancing using smote or undersampling, as well as unsupervised learning techniques, can improve model performance. Improving the model through feature engineering and hyperparameter tuning is also needed to improve the effectiveness of fraud detection.

References

Ismawati, Fatah Z. Penggunaan Data Mining Untuk Mendeteksi Penipuan Transaksi Kartu Kredit Algoritma Decision Tree. Jamastika. 2025;4(1):95–101.

Syahbani AM, Firdaus W, Musodo KA. A Comparative Study of Data Mining Algorithms for Fraud Detection in Financial Transactions. Sinkron. 2025;9(2):814–21.

Eldo H, Ayuliana A, Suryadi D, Chrisnawati G, Judijanto L. Penggunaan Algoritma Support Vector Machine (SVM) Untuk Deteksi Penipuan Pada Transaksi Online. J Minfo Polgan. 2024;13(2):1627–32.

Paramitha AF, Arimbi YD, Riyanto S, Apriani NF, Siagian AHAM. Classification of Suspicious Financial Transactions Using Light Gradient Boosting Machine Method (LGBM) Based on Social Network Analysis (SNA) Indicators. Sistemasi. 2024;13(2):572.

Aslam F. Advancing Credit Card Fraud Detection: A Review of Machine Learning Algorithms and the Power of Light Gradient Boosting. Ajcst. 2024;

Jiang Y. Credit Card Transaction Fraud Detection Based on Machine Learning. Appl Comput Eng. 2024;51(1):73–80.

Hassan H, Ahmad MA, Mustapha R. An Enhanced Feature Engineering Technique for Credit Card Fraud Detection. Fudma J Sci. 2024;8(4):8–16.

Esenogho E, Mienye ID, Swart TG, Aruleba K, Obaido G. A Neural Network Ensemble With Feature Engineering for Improved Credit Card Fraud Detection. Ieee Access. 2022;10:16400–7.

Leevy JL, Hancock J, Khoshgoftaar TM. Comparative Analysis of Binary and One-Class Classification Techniques for Credit Card Fraud Data. J Big Data. 2023;10(1).

Guo L, Song R, Wu J, Xu Z, Zhao F. Integrating a Machine Learning-Driven Fraud Detection System Based on a Risk Management Framework. Appl Comput Eng. 2024;87(1):80–6.

Balogun O, Kupolusi JA, Akomolafe A. Credit Card Fraud Detection Using Machine Learning Algorithms. Br J Comput Netw Inf Technol. 2024;7(3):1–35.

Dai S. Research on Detecting Credit Card Fraud Through Machine Learning Methods. 2023;1030–7.

Khatun MS, Alam BR, Taslim M, Hossain MA. Handling Class Imbalance in Credit Card Fraud Using Various Sampling Techniques. Am J Multidiscip Res Innov. 2022;1(4):160–8.

Akazue MI, Debekeme IA, Edje AE, Asuai CE, Osame UJ. UNMASKING FRAUDSTERS: Ensemble Features Selection to Enhance Random Forest Fraud Detection. J Comput Theor Appl. 2023;1(2):201–11.

Fernandes RJ. Queue Fundamentals, Implementation and Its Applications in Round Robin Scheduling. Int J Adv Sci Eng. 2022;9(1):2556–66.

Cheng Y, Yao X. Carbon intensity reduction assessment of renewable energy technology innovation in China: A panel data model with cross-section dependence and slope heterogeneity. Renew Sustain Energy Rev [Internet]. 2021;135.

Zhang Z, Hu Q, Yin J. Maritime-Accident-Induced Environmental Pollution and Economic Loss Analysis Using an Interpretable Data-Driven Method. Sustainability. 2025;17(7):3023.