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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmu Komputer dan Agri-Informatika Indonesian Journal of Artificial Intelligence and Data Mining Dinamisia: Jurnal Pengabdian Kepada Masyarakat Building of Informatics, Technology and Science KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Pusat Inovasi Masyarakat
Hasibuan, Lailan Sahrina
Department Of Computer Science, Bogor Agricultural University
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Social Sciences Other

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Credit Card Fraud Detection Using Support Vector Machine: A Study on Data Balancing Strategies Hasibuan, Lailan Sahrina
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8514

Abstract

The rise in credit card transactions has been accompanied by an increase in fraudulent activities. One of the key challenges in detecting fraud is the distribution of the dataset, where fraudulent transactions are significantly outnumbered by normal ones. Despite their low occurrence, fraudulent transactions have a significant impact on the banking sector. Therefore, an effective model is needed to identify and estimate fraudulent transactions. This study aims to generate optimal training dataset from an imbalanced one using Adaptive Synthetic Sampling (ADASYN) to enhance the training process of Support Vector Machine (SVM) model. The dataset used consists of anonymized credit card transactions and labeled as either fraudulent or normal, sourced from the Kaggle dataset. It contains transactions made by European cardholders in September 2013, covering a two-day period with 492 fraud cases out of 284,807 transactions. Three datasets were derived from the original: raw, balanced, and support vector-based balanced. The SVM model training on these datasets resulted in sensitivities of 0.39, 0.64, and 0.70, respectively, while the precision values were 0.92, 0.72, and 0.01. The corresponding f-measure values were 0.55, 0.68, and 0.02. The best performance based on the f-measure was achieved using the balanced version of the raw dataset.
Co-Authors Ahmad Fariqi Aisya, Zalfa Rihadatul Annisa, Apriyanti Nur Aziz Kustiyo Chamdan L Abdulbaaqiy Dewi, Maharani Arzela Endang Purnama Giri Fajar, Muhammad Rizky Hari Agung Adrianto Imas Sukaesih Sitanggang Jannah, Fatimah Alfiatul Lilik Prayitno Lina Herlina Tresnawati Mala Nurilmala Melly Br Bangun Muhammad Abrar Istiadi Muhammad Asyhar Agmalaro Muhammad Murtadha Ramadhan Nurdevi Noviana Nurul Hudachair Prayitno, Lilik Putra, Hilal Rosyid Resnawati RR. Ella Evrita Hestiandari Salsabila, Kania Sambogo, Rizki Bagus Sarah, Wurianty Sita Nabila Sony Hartono Wijaya Syahrani, Iga Dwi Wisnu Ananta Kusuma Yanda Novialdi