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All Journal Jurnal Sistem Informasi Mahakarya (JSIM) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Green Engineering: Journal of Engineering and Applied Science Global Science: Journal of Information Technology and Computer Science Technema: Journal of Intelligent Engineering and Computing
Erlita Sulistiati
Universitas Mahakarya Asia
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : technema journal of intelligent engineering and computing

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Credit Card Fraud Detection Using Random Forest and XGBoost on a Public Kaggle Dataset Erlita Sulistiati; Tuti Susilawati
Technema: Journal of Intelligent Engineering and Computing Vol. 1 No. 1 (2026): March: Technema: Journal of Intelligent Engineering and Computing
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

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Abstract

Credit card fraud detection remains a critical challenge in digital financial ecosystems characterized by extreme class imbalance and evolving attack patterns. This study adopts an empirical experimental design to evaluate and compare Random Forest and XGBoost models using the publicly available Kaggle Credit Card Fraud Detection dataset. A controlled pipeline incorporating stratified data splitting, SMOTE-based imbalance mitigation, and GridSearchCV hyperparameter optimization was implemented to ensure methodological consistency and reproducibility. Performance was assessed through precision, recall, F1-score, ROC-AUC, confusion matrix analysis, and computational efficiency metrics. Results indicate that XGBoost outperformed Random Forest in recall, F1-score, and ROC-AUC, demonstrating enhanced minority-class discrimination and reduced false negatives under optimized conditions. Random Forest exhibited competitive precision and interpretability transparency, though with slightly lower sensitivity. Scalability evaluation confirmed that both models maintained low inference latency suitable for near-real-time deployment. The findings highlight the critical role of imbalance handling and parameter optimization in ensemble-based fraud detection and support boosting-oriented approaches as strategically advantageous for operational financial security systems.  
Digital Twin Technology for Sustainable Industrial Operations Erlita Sulistiati; Bustomi Bustomi; Guslila Sari Nasution; Atina Salamah; Rian Ardianto
Technema: Journal of Intelligent Engineering and Computing Vol. 1 No. 2 (2026): : June: Technema: Journal of Intelligent Engineering and Computing
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

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Abstract

Digital Twin technology has emerged as a strategic enabler for sustainable industrial transformation by integrating physical operations, virtual representations, predictive analytics, and sustainability-oriented decision support into a unified cyber–physical environment. This study aims to develop and analytically evaluate a comprehensive Digital Twin framework capable of supporting sustainable industrial operations through the integration of operational efficiency, energy performance, resource optimization, and system resilience dimensions. A non-empirical system design approach was employed to construct a multilayer architecture consisting of physical operation, data acquisition, communication and synchronization, digital twin modeling, analytics and optimization, and sustainability decision-support layers. Technical evaluation was conducted through model-based simulation and analytical assessment using standardized sustainability and operational indicators. The findings demonstrate that the proposed framework strengthens operational visibility, predictive maintenance capability, energy efficiency, resource utilization, responsiveness, and resilience through continuous interaction between physical and virtual environments. The analysis further indicates that Digital Twin integration facilitates circularity, sustainability governance, and Industry 5.0 readiness by enabling adaptive and data-driven industrial decision making. The study contributes a holistic conceptual framework that advances the understanding of Digital Twin technology as a sustainability-enabling infrastructure for future industrial systems.  
Co-Authors Abdul Azis Abdurrahman Abdurrahman Ahmad Jurnaidi Wahidin Atina Salamah Bustomi Bustomi Dewi Mulyama Edwar Ali Enda Ribka Meganta P Guslila Sari Nasution Muhamad Furqon Rian Ardianto Rusidi Rusmin Saragih, Rusmin sri hartati Suyahman Suyahman Tiwuk Widiastuti Trisnawan, Ahmad Budi Tuti Susilawati Widiastuti, Rosalina Yani