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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

D. Anggara

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4 papers indexed here

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Conference Aug 2026

TLINGA: A Local-First Urban Intelligence Framework for Social Media Signal-to-Action Recommendation

Citizen aspirations are important inputs for responsive local governance, yet formal complaint channels require structured reporting, while conventional social-media monitoring often focuses only on sentiment and trends. This paper proposes TLINGA, a Local-First Urban Intelligence Framework that transforms naturally ex...

O. M. Prabowo, Brian Al Afwan Izhar, D. Anggara et al. · 0 citations
Conference Aug 2026

A Comparative Study of Pre-trained Language Models for Cognitive Distortion Detection in Indonesian Text

Cognitive distortions are maladaptive thinking patterns that are closely associated with anxiety, depression, and other mental health disorders. Early detection of these distortive thoughts through textual expressions has the potential to support scalable mental health preventive actions. However, the comparative study...

Aina Nur Fadlilah, S. Supangkat, D. Anggara · 0 citations
Conference Aug 2026

Prediction-Conditioned RAG: A Data-Driven Digital Twin Approach for Diabetes Clinical Decision Support in Resource-Constrained Settings

Diabetes management in Indonesia remains largely reactive, constrained by costly continuous glucose monitoring (CGM), uneven electronic health record (EHR) adoption, and reliance on manual self-monitoring logbooks. State-of-the-art digital twin frameworks for glycemic management achieve strong predictive accuracy but r...

Daffari Adiyatma, S. Supangkat, D. Anggara · 0 citations
Conference Aug 2026

Development of a Machine Learning Model and Explainable Artificial Intelligence for Hypertension Risk Estimation System

Hypertension is a leading modifiable risk factor for cardiovascular disease, yet its management remains largely reactive, so many cases are detected only after complications arise. Machine learning (ML) can estimate hypertension risk earlier, but the black-box nature of high-performing models hinders clinical trust; fu...

Samuel Franciscus T. H, S. Supangkat, D. Anggara · 0 citations

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