Article Per Year (5 Year)
p-Index From 2021 - 2026
1.789
P-Index
This Author published in this journals
All Journal Jurnal Sains dan Teknologi PROSIDING CSGTEIS 2013 Prosiding SNATIF Jurnal Sistem dan Informatika Seminar Nasional Informatika (SEMNASIF) WIDYABHAKTI Jurnal Ilmiah Populer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Teknologi Dan Sistem Informasi Bisnis IT (INFORMATIC TECHNIQUE) JOURNAL
Wulaning Ayu, Putu Desiana
STMIK STIKOM Bali
Aerospace Engineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 21 Documents
Search

 1   2   3 
Analisis Performa Komparatif Algoritma Machine Learning untuk Deteksi Fraud dalam Transaksi Blockchain Ni Putu Eka Apriyanthi; Civica Moehaimin Dhewanty; Putu Desiana Wulaning Ayu; I Made Riyan Adi Nugroho; I Wayan Rizky Wijaya
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2285

Abstract

The decentralized finance (DeFi) and blockchain environment encounters substantial security threats, particularly complex and expensive fraudulent activities. Conventional detection methods frequently prove insufficient when dealing with enormous transaction volumes and datasets characterized by unbalanced class distributions. This research seeks to examine and evaluate the effectiveness of three widely used machine learning techniques Logistic Regression, Random Forest, and XGBoost in identifying fraudulent activities within blockchain transactions. The investigation utilized an Ethereum transaction dataset sourced from Kaggle, where the imbalanced data distribution was addressed through the application of SMOTE methodology. Performance assessment was carried out using precision, recall, F1-score, and ROC-AUC measurements on testing data. The findings demonstrate XGBoost's superiority among the algorithms, delivering an accuracy rate of 99.46%, precision of 99.69%, recall of 97.86%, and ROC-AUC score of 99.97%, while maintaining minimal false positive occurrences (only 1 instance). These results exceeded those achieved by both Random Forest and Logistic Regression models, demonstrating that gradient boosting methodologies excel at detecting intricate fraudulent behaviors. The study's outcomes offer significant contributions toward creating resilient and autonomous fraud detection frameworks. Keywords: Blockchain, Fraud, Machine Learning, Logistic Regression, Random Forest, XGBoost.
Co-Authors Artamerta, Aditya Naray Civica Moehaimin Dhewanty Dandy Pramana Hostiadi Gede Angga Pradipta Gede Angga Pradipta, Gede Angga Gede, Angga Pradipta I Gede Putu Krisna Juliharta I Komang Agus Ady Aryanto I Made Darma Susila I Made Darma Susila, I Made Darma I Made Liandana I Made Riyan Adi Nugroho I Wayan Rizky Wijaya Intaran, Arya Ngurah Kristianto Kristianto Made Liandana Made, Liandana Mahajaya, Nyoman Sarasuartha Muhammad Riza Hilmi Ni Luh Putri Srinadi Ni Putu Eka Apriyanthi Nyoman Ayu Nila Dewi Roy Rudolf Huizen Tubagus Mahendra Kusuma Yohanes Priyo Atmojo