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All Journal Scientific Journal of Informatics JITK (Jurnal Ilmu Pengetahuan dan Komputer) International Journal of Engineering, Technology and Natural Sciences (IJETS) BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jurnal Ilmu Komputer dan Informatika J-ESA (Jurnal Ekonomi Syariah) Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Taroa : Jurnal Pengabdian Masyarakat SEWAGATI: Jurnal Pengabdian Kepada Masyarakat Abdi Masyarakat UIKA Journal of Excellence Humanities and Religiosity Jurnal Pengabdian Nusantara Jurnal Pengabdian kepada Masyarakat (PEMAS) Scientific: Journal of Computer Science and Informatics Journix: Journal of Informatics and Computing Indonesian Applied Research Computing and Informatics
Sutriawan
Department Of Computer Science, Universitas Muhammadiyah Bima, Indonesia
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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.
Co-Authors Ade Kurniawan Afril Efan Pajri Ahmad Zainul Fanani Ahmad Zaniul Fanani Aldillah Alkhair Alzami, Farrikh Astuti, Rahma Hayuning Budiman Budiman Enggar Novianto Erham Erham Erviana, Irma Fathir Fathir Fathir Fathurrahman Fathurrahman Fitriani Ramadhani Gufran Gufran, Gufran Hamiah, Nurul Husnul Khatimah, Nur Khairunnas Khairunnas, Khairunnas Khairunnisa Khairunnisa Khairunnisa Khatimah, Nur Husnul Kukuh Yudhistiro, Kukuh Lailia Rahmawati LUKMAN, LUKMAN M. Fikram Muammar Iksan Muh. Alimin Muhammad Akbar Muhammad Amirul Mu'min Muhammad Hayun Muljono Muljono Mursalim Mursalim Nimas Ratna Sari Nur Hunul Khatimah Nur Husnul Khatimah Nur Wahyu Hidayat Nurfidianty Annafi, Nurfidianty Putra, Firnanda Al Islama Achyunda Rahmawati, Lailia Randitha Missouri Rosyid, Romadhoni Ruri Suko Basuki Sahrul Ramadhan Sanusi, Gufran Siti Mutmainah Taufik Azhary Teguh Ansyor Lorosae Teguh Ansyor Lorosae Ummu Rofikah Widayati, Ulfa Zumhur Alamin Zumhur Alamin Zuraini, Irma