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All Journal Jurnal Bumigora Information Technology (BITe) Afeksi: Jurnal Penelitian dan Evaluasi Pendidikan Jurnal Sains Informatika Terapan (JSIT) Insand Comtech : Information Science and Computer Technology Journal Proceeding of International Conference Health, Science And Technology (ICOHETECH) JSITIK: Jurnal Sistem Informasi dan Teknologi Informasi Komputer Journal of Engineering and Science Application
Guterres, Juvinal Ximenes
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Social Sciences Other

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Fraud Prediction in Online Financial Transactions with a Combination of SMOTE and Ensemble Classifier guterres, juvinal Ximenes; Silva, Delfim Da; Sales, Jacinto Defatima; Freitas, Abrao; Costa, Nuno da
Jurnal Sains Informatika Terapan Vol. 5 No. 1 (2026): Jurnal Sains Informatika Terapan (Februari, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

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Abstract

Detecting fraudulent transactions remains a major challenge in digital financial systems due to the severe imbalance between legitimate and fraudulent records. This study aims to develop a classification model capable of identifying fraudulent transactions with high sensitivity to minority classes, while ensuring performance stability suitable for operational deployment. The methodology includes data preprocessing through outlier removal, feature normalization, and stratified data partitioning. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied to generate representative synthetic samples for the minority class. Multiple machine learning algorithms are evaluated, including Random Forest, Decision Tree, Bagging, Gradient Boosting, Logistic Regression, Neural Network, K-Nearest Neighbors, and Support Vector Machine. Model performance is assessed using Precision, Recall, F1-Score, AUC, and G-Mean. The results show that the proposed approach achieves stable and reliable performance, with an AUC of 0.89 and a G-Mean of 0.81, demonstrating its effectiveness for operational fraud detection and error minimization.
Co-Authors Abu Tholib Agustina Srirahayu Andi, Moh syaiful Arnes Yuli Vandika Costa, Nuno da da Silva, Recardina Freitas Freitas, Abrao Gusmao, Adozinda Soares Guterres, Anita Hairani Hairani Jauhari, Burhanuddin Khairan marzuki Lalu Ganda Rady Putra Mardedi, Lalu Zazuli Azhar Mardedi, Lalu Zazuli Azhar Maulindar, Joni Muhammad Najibulloh Muzaki Muhammad Syarif Hartawan Noviyanty Pamekas, Bondan Wahyu Riska Rosita Sales, Jacinto Defatima Shudiq, Wali Ja'far Silva, Delfim Da Siswanto, Yoyok Adi Soares, Domingas Solihin Solihin Sucipto Sucipto Sukron, Moh