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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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Journal : Building of Informatics, Technology and Science

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Deteksi Penipuan Kartu Kredit Menggunakan Support Vector Machine dengan Optimasi Grid Search dan Genetic Algorithm Hasibuan, Lailan Sahrina; Jannah, Fatimah Alfiatul
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5355

Abstract

Credit card transactions have increased significantly every year. Along with the increasing use of credit cards, the risk of fraud by irresponsible people also increases. Credit card fraud can be detected with the help of machine learning. The main problem that often encountered is the transaction data has very large dimensions, unbalanced classes, and requires a detection process with a short computation time. Therefore we need a model that can produce good performance with short computation time using the support vector machine (SVM) method with grid search and genetic algorithm optimization. From the three models built, it was found that the SVM model using an initial dataset which was balanced using ADASYN and searching for the best parameters using grid search as a hyperparameter optimization technique was able to carry out good detection and short computing time. This model is able to detect fraudulent transactions with 99% sensitivity and 99% specificity and the shortest model training time among the other two models.
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