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All Journal Global Science: Journal of Information Technology and Computer Science Indonesian Applied Research Computing and Informatics
Mursalim Mursalim
Universitas Sugeng Hartono
Computer Science & IT Control & Systems Engineering

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Unsupervised Credit Card Fraud Detection Using Autoencoder-Based Anomaly Detection on Highly Imbalanced Transaction Data Mursalim Mursalim; Sutriawan Sutriawan; Nimas Ratna Sari; Nur Wahyu Hidayat; Zumhur Alamin
Indonesian Applied Research Computing and Informatics Vol. 1 No. 2: December (2025)
Publisher : PT. Teras Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64479/iarci.v1i2.64

Abstract

Credit card fraud detection is a critical problem in the financial sector, primarily due to its direct correlation with financial liability and the preservation of user integrity. A major challenge in fraud detection is the extreme class imbalance, where fraudulent transactions are rare compared to legitimate ones, causing supervised approaches to require sufficient labeled fraud data and often become biased toward the majority class. This study proposes an unsupervised anomaly detection approach based on an Autoencoder to identify fraudulent transactions in highly imbalanced credit card transaction data. The Autoencoder is trained exclusively on normal transactions to learn representative patterns of legitimate behavior. During inference phase, transactions exhibiting elevated reconstruction error relative to established norms are designated as anomalies, indicative of potential fraud. The experiments use the Credit Card Fraud Detection dataset from Kaggle, containing 284,807 transactions: 284,315 normal (99.828%) and 492 fraudulent (0.172%). The workflow includes numerical feature normalization for the Time and Amount attributes, splitting normal data into training and validation sets, selecting an anomaly threshold based on the reconstruction error distribution, and evaluating performance using metrics suitable for imbalanced data such as precision, recall, and F1-score. The results indicate that the proposed unsupervised Autoencoder offers an effective alternative when labeled fraud examples are limited, by detecting deviations from normal transaction patterns through reconstruction behavior.
Energy Aware Software Architecture Optimization Using Real Time Analytics and Self Adaptive Control in Intelligent Computing Systems Ardy Wicaksono; Mursalim Mursalim; Arif Tri Widiyatmoko; Deny Prasetyo; Ahmad Budi Trisnawan; Yanuar Wicaksono
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 4 (2025): December: Global Science: Journal of Information Technology and Computer Scienc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i4.195

Abstract

The increasing demand for intelligent computing systems, including cloud computing, artificial intelligence (AI), and the Internet of Things (IoT), has resulted in a significant rise in energy consumption, which poses both environmental and economic challenges. The high computational power required by these systems, coupled with the continuous operation of data centers and connected devices, has led to inefficiencies in energy usage. This paper explores the integration of real time analytics and self adaptive control mechanisms to optimize energy consumption in intelligent systems. By employing advanced software tools for real time monitoring, dynamic adjustments based on workload conditions, and adaptive algorithms for energy optimization, significant reductions in power usage were achieved without compromising system performance. The optimized architecture dynamically adjusts system parameters such as processor frequency, task scheduling, and voltage to ensure efficient energy consumption during varying operational demands. The results show a 24% reduction in energy usage during low demand periods, demonstrating the potential of real time energy management strategies. The study also compares the optimized architecture with conventional static systems, highlighting the benefits of dynamic energy management, including improved performance balance, reduced environmental impact, and lower operational costs. These findings suggest that the integration of energy efficient practices in software design, particularly through real time analytics and self adaptive mechanisms, offers a sustainable solution for modern computing systems. Future research could focus on improving self adaptive systems, incorporating renewable energy sources, and expanding the approach to other intelligent systems, such as autonomous vehicles or large scale smart grids. The practical applications of this research are vast, particularly in large scale applications such as data centers and cloud computing, where energy efficiency is critical for sustainability.
Co-Authors Ardy Wicaksono Arif Tri Widiyatmoko Deny Prasetyo Nimas Ratna Sari Nur Wahyu Hidayat Sutriawan Trisnawan, Ahmad Budi Yanuar Wicaksono Zumhur Alamin