PENERAPAN METODE SUPERVISED LEARNING DAN TEKNIK RESAMPLING UNTUK PREDIKSI PENIPUAN TRANSAKSI KEUANGAN

CONSTANCIO, ELVEN and Tania, Ken Ditha (2024) PENERAPAN METODE SUPERVISED LEARNING DAN TEKNIK RESAMPLING UNTUK PREDIKSI PENIPUAN TRANSAKSI KEUANGAN. Undergraduate thesis, Sriwijaya University.

	Image RAMA_57201_09031382126169_cover.jpg - Accepted Version Restricted to Repository staff only Download (224kB) | Request a copy
	Text RAMA_57201_09031382126169.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (4MB) | Request a copy
	Text RAMA_57201_09031382126169_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy
	Text RAMA_57201_09031382126169_0018078502_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (761kB)
	Text RAMA_57201_09031382126169_0018078502_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (319kB) | Request a copy
	Text RAMA_57201_09031382126169_0018078502_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (388kB) | Request a copy
	Text RAMA_57201_09031382126169_0018078502_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (508kB) | Request a copy
	Text RAMA_57201_09031382126169_0018078502_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (235kB) | Request a copy
	Text RAMA_57201_09031382126169_0018078502_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (197kB) | Request a copy
	Text RAMA_57201_09031382126169_0018078502_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy

Abstract

Financial transaction fraud can result in devastating consequences for the stability of companies, as well as huge losses for shareholders, the industry, and even the market as a whole. As fraud in financial transactions increases, there is a need for effective methods to accurately detect and prevent fraudulent activities. This study aims to compare the performance of five machine learning models, namely Random Forest, K-Nearest Neighbors (KNN), Decision Tree, XGBoost, and Extra Trees, in detecting financial transaction fraud using an imbalanced dataset. To overcome the data imbalance problem, three resampling techniques are applied, namely Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), and Undersampling. Experiments were conducted with two training and test data sharing ratios, namely 70:30 and 80:20. The evaluation results showed that the XGBoost model was the most consistent, with the highest ROC AUC value of 99%, especially after the application of resampling techniques. The 80:20 data ratio resulted in a more balanced distribution and better model performance in detecting the minority class, particularly after resampling. This study concludes that the XGBoost model with resampling techniques is highly effective in addressing data imbalance.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Penipuan Transaksi Keuangan, SMOTE, ADASYN, Undersampling, XGBoost
Subjects:	T Technology > T Technology (General) > T57.6-57.97 Operations research. Systems analysis
Divisions:	09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User:	Elven Constancio
Date Deposited:	07 Jan 2025 08:36
Last Modified:	07 Jan 2025 08:36
URI:	http://repository.unsri.ac.id/id/eprint/162912

Actions (login required)

View Item