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Panji Arisaputra

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Enhancing Cardiovascular Risk Stratification with Interpretable Ensemble Learning: A Comparative Analysis and Explainable AI-Driven Insights

Results show that computationally efficient and interpretable ensembles provide a viable alternative to more complex models, particularly in data-constrained clinical settings, and highlight the potential of interpretable ensemble learning to develop dependable, deployable clinical decision support tools for integratio...

Panji Arisaputra, Pandu Wicaksono, Karli Eka Setiawan et al. · 0 citations
Conference Jul 2026

Multi-Objective Optimization for Credit Card Fraud Classification: Achieving a Balance between Accuracy and Carbon Footprint within a Green AI Framework

As financial fraud becomes increasingly sophisticated, the demand for complex machine learning models has surged, inadvertently leading to a significant increase in computational energy consumption. This study addresses the critical trade-off between predictive accuracy and environmental sustainability within a Green A...

Ebenhaezer George Renaldi Muljadi, Albert Justin, F. Ardan et al. · 0 citations

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