Comparison of Random Forest and K-Nearest Neighbors (KNN) Algorithms in Detecting Credit Card Transaction Anomalies

Fitria; Emy Iryanie; Heldalina; Muhammad Syahid Pebriadi; Heru Kartika Candra

doi:10.59890/ijir.v3i7.49

Authors

Fitria Politeknik Negeri Banjarmasin
Emy Iryanie Politeknik Negeri Banjarmasin
Heldalina Politeknik Negeri Banjarmasin
Muhammad Syahid Pebriadi Politeknik Negeri Banjarmasin
Heru Kartika Candra Politeknik Negeri Banjarmasin

DOI:

https://doi.org/10.59890/ijir.v3i7.49

Keywords:

Random Forest, K-Nearest Neighbors (KNN), Machine Learning, Transaction Anomalies

Abstract

This study aims to compare two machine learning algorithms, namely Random Forest and K-Nearest Neighbors (KNN), in detecting anomalies in credit card transactions for fraud detection purposes. The dataset used consists of credit card transactions that include features such as the number of transactions, transaction types, sender and recipient balances. The results of the study show that Random Forest excels in terms of accuracy and ability to distinguish legitimate and fraudulent transactions compared to KNN. Although KNN has advantages in terms of speed and interpretability, the model's performance declines on large and unbalanced data. Instead, Random Forest can better address class imbalances and data complexity, resulting in more stable and more accurate models. Based on these findings, Random Forest is more recommended for use in credit card fraud detection applications, while KNN can be considered for simpler applications or smaller data. This research provides useful insights for the development of machine learning-based fraud detection systems in the financial sector

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