Skripsi

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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407007119	T162912	T1629122024	Central Library (REFERENS)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1629122024

Publisher

Indralaya : Prodi Sistem Informasi, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xix. 44 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

003.010 7

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Sistem Informasi
Identifikasi Sistem--Teknik resampling

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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