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All Journal Jurnal Sains Informatika Terapan (JSIT)
juvinal Ximenes guterres
Universidade Oriental Timor Lorosae UNITAL
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i1.894

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.
Patient Prevention Prediction and Diagnosis Using Data Mining in Healthcare Quality Management Noviyanty; juvinal Ximenes guterres; Adozinda Soares Gusmao; Domingas Soares; Anita Guterres; Recardina Freitas da Silva
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

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Abstract

The expansion of digital medical records and clinical data has strengthened the development of intelligent analytical systems to support early disease detection and improve diagnostic accuracy. This study aims to evaluate the performance of three classification algorithms, namely Random Forest, Support Vector Machine, and Logistic Regression, in predicting stroke risk using multidimensional patient clinical information. The dataset consists of 224 patient records derived from the Kaggle Stroke Dataset and additional questionnaire data collected from hospitals and primary health centers. The variables include demographic characteristics, clinical history, lifestyle factors, and physiological indicators. The research methodology involves several stages, including data preprocessing, feature selection using ANOVA F value, class balancing through the Synthetic Minority Oversampling Technique, model training, and performance evaluation using Accuracy, Precision, Recall, F1 Score, Matthews Correlation Coefficient, and Area Under the Curve. The results indicate that the Random Forest model achieves the highest performance, with an accuracy of 0.91 and an Area Under the Curve of 0.91, outperforming Support Vector Machine and Logistic Regression. This outcome confirms the effectiveness of ensemble based approaches in identifying complex nonlinear patterns and managing imbalanced data. The study contributes to healthcare quality improvement by providing a reliable prediction framework that supports early clinical decision making, reduces diagnostic delays, and enhances patient care outcomes.
Co-Authors Abrao Freitas Adozinda Soares Gusmao Anita Guterres Delfim Da Silva Domingas Soares Jacinto Defatima Sales Noviyanty Nuno da Costa Recardina Freitas da Silva