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Author

Nur Alamsyah

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Open access Aug 2026

SHAP-ALE: A Novel Approach to Explainability in Mental Health Prediction using RFR

This study highlights the contribution of SHAP-ALE as a hybrid explainability framework that integrates local and global interpretability, enabling a more comprehensive understanding of feature interactions and non-linear effects beyond the capabilities of individual methods.

Nur Alamsyah · 0 citations
Jul 2026

Toward Intelligent Accounting: An Explainable Machine Learning Framework for Risk-Oriented Transaction Outcome Prediction

The evaluation of accounting transactions is increasingly challenging due to the growing volume of financial records, severe class imbalance, and the limited transparency of existing audit support systems. Many current machine learning approaches emphasize prediction accuracy while providing insufficient interpretabili...

J. K. Siregar, Astari Dianty, Antonius Bimo Rentor et al. · 0 citations

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