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Author

Satrio Adi Priyambada

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Conference Sep 2026

A Comparative Study of Machine Learning Regression Models for Predicting Remote Worker Productivity: Accuracy, Robustness, Efficiency, and Interpretability

Remote and hybrid work has become the norm, yet the tools organizations use to gauge employee productivity have not kept pace, leaving many to lean on indirect proxies of uneven accuracy. This study investigates whether present-day Machine Learning (ML) regression can reliably measure and forecast remote-worker product...

Hengki Muradi, Rumadi Rumadi, Satrio Adi Priyambada et al. · 0 citations
Conference Sep 2026

Low Carbon Considerations in Space Weather AI: Weighted Ensemble Feature Selection for FoF2 Forecasting

The increasing computational cost of machine learning models impacts carbon emissions. A more complex model produces more $\mathbf{C O}_{\mathbf{2}}$ and requires longer training times. This poses a significant challenge in predicting the critical frequency of the F2 layer (foF2), a crucial parameter for HF radio commu...

Fauzan Zacky Firmansyah, Satrio Adi Priyambada, V. Dear et al. · 0 citations

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