Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal Journal of Renewable Engineering Technologia Journal
Hasbu Naim Syaddad
Universitas Komputer Indonesia
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Integrating AI-Driven Advanced Knowledge Management Systems to Mitigate Civil Servants' Risk Andi Agus Salim; Hasbu Naim Syaddad; Luki Ishwara
Technologia Journal Vol. 3 No. 3 (2026): Technologia Journal-August
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/rftf7g69

Abstract

The rapid digitalization of public administration has positioned artificial intelligence (AI) as a strategic lever for strengthening institutional knowledge and reducing operational risk among civil servants. Yet public organizations continue to struggle with fragmented knowledge repositories, tacit knowledge loss due to workforce turnover, inconsistent decision-making, and exposure to compliance, legal, and reputational risks arising from manual and siloed information practices. This study examines how an AI-Driven Advanced Knowledge Management System (AI-AKMS) can be integrated into civil service institutions to mitigate such risks. Using a systematic literature review of twenty-five peer-reviewed sources published between 2021 and 2026, the study synthesizes evidence on AI-enabled knowledge capture, retrieval-augmented generation, predictive risk analytics, and generative AI governance in public administration. The novelty of this study lies in proposing an integrated conceptual framework that links AI-based knowledge management functions directly to specific civil-service risk categories, namely compliance risk, decision risk, knowledge-continuity risk, and reputational risk, an integration rarely addressed jointly in prior literature. Findings indicate that AI-AKMS adoption improves knowledge retrieval accuracy, accelerates policy compliance checking, and strengthens organizational resilience, provided that governance, data quality, and human oversight mechanisms are institutionalized. The study concludes with practical implications for public-sector digital transformation strategy and identifies avenues for future empirical validation.
A Three-Layer Cyber AI Governance Framework for Accountable Reinforcement Learning in National Data Sovereignty Andi Agus Salim; Hasbu Naim Syaddad; Luki Ishwara; Agus Nursikuwagus; Handoko Handoko; Rio Yunanto
Journal of Renewable Engineering Vol. 3 No. 4 (2026): JORE - August
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/8h6yam07

Abstract

The rapid deployment of reinforcement learning (RL) agents in critical national infrastructure has outpaced the governance instruments designed to hold them accountable, creating a widening gap between algorithmic autonomy and sovereign oversight. This article proposes a Three-Layer Cyber-AI Governance Framework that integrates the technical, organizational, and regulatory dimensions of accountability for RL systems operating within national data sovereignty regimes. Using a systematic literature review guided by PRISMA 2020 procedures, twenty-five peer-reviewed and preprint sources published between 2021 and 2026 were analyzed through thematic synthesis to identify recurring governance constructs across cybersecurity, AI ethics, and data-sovereignty scholarship. The synthesis reveals three interdependent layers: an Algorithmic Layer governing reward design, explainability, and adversarial robustness; an Organizational Layer governing human oversight, audit trails, and incident reporting; and a Sovereign-Regulatory Layer governing data localization, cross-border data flow, and international cooperation. The proposed framework departs from existing layered models by embedding a continuous feedback loop that links real-time algorithmic telemetry to national regulatory review, closing the accountability gap that single-layer or purely technical frameworks leave open. The article concludes that accountable reinforcement learning under conditions of national data sovereignty requires coordinated, multi-layer instruments rather than isolated technical fixes, and it outlines an agenda for empirical validation of the framework across diverse regulatory contexts.   
Co-Authors Agus Nursikuwagus Andi Agus Salim Handoko Handoko Luki Ishwara Rio Yunanto