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All Journal Media Jurnal Informatika Journal of Renewable Engineering Technologia Journal JRSI (Jurnal Rekayasa Sistem dan Industri) Big Data Analytics and Data Science
Andi Agus Salim
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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Classification, Prediction, and Prescription of Digital Government Governance Maturity Levels: Leveraging SPBE Index Data (2019–2024) for Evidence-Based Regional Digital Government Architecture Planning in Indonesia Andi Agus Salim; Zainal Arifin Hasibuan; Agus Nursikuwagus; Sri Supatmi
Big Data Analytics and Data Science Vol. 1 No. 2 (2026): June: Big Data Analytics and Data Science
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/bdas.v1i2.409

Abstract

Indonesia's transition from the SPBE evaluation framework to the 2025–2029 Pemdi (Digital Government) Index marks a strategic shift toward comprehensive governance maturity. However, regional governments face significant challenges in strategic planning due to the absence of empirical models linking historical SPBE performance to future Pemdi trajectories and a lack of data-driven guidance for prioritizing governance interventions. This research aims to develop an integrated Classification-Prediction-Prescription (CPP) framework to classify, forecast, and prescribe regional digital government governance maturity levels. The proposed methodology employs machine learning algorithms (Random Forest and Gradient Boosting) to conduct multi-class classification (five maturity levels) and regression (continuous score prediction) using longitudinal SPBE data (2019–2024) from 548 Indonesian regional governments. This quantitative approach is complemented by feature importance analysis and scenario-based simulations to generate actionable insights. The models are projected to achieve over 85% classification accuracy and a regression RMSE of under 0.5. The synthesis of main findings reveals that indicators within the policy and architecture planning domains are the strongest predictors driving maturity progression. Furthermore, the study segments regional governments into four distinct trajectory clusters and formulates a tailored prescriptive recommendation matrix across multiple planning horizons. In conclusion, the CPP framework effectively translates national evaluation data into actionable intelligence, empowering regional governments to optimize resource allocation, prioritize high-impact interventions, and systematically align their digital transformation pathways with formal planning documents such as the RPJMD and Regional Action Plans.
The Navigating the Data Labyrinth: A Bibliometrix of Data Governance Challenges in Implementing Digital Twins for Disaster Management in Developing Countries Andi Agus Salim; Hasbu Naim Syaddad; Luki Ishwara; Irawan; Estiko Rijanto; Irfan Dwiguna
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6295

Abstract

Indonesia faces significant disaster risks due to its location in the Ring of Fire, necessitating advanced mitigation technologies like Digital Twins (DT). However, the effectiveness of DT relies heavily on real-time data integration, which is often hindered by governance issues rather than technological capability. This study aims to identify specific data governance challenges in adopting DT for the public sector specifically in disaster management and proposes a conceptual framework suitable for developing countries, using Indonesia as the primary representative case. A Bibliometric analysis was conducted using the PRISMA protocol. Data was collected from Scopus (n=107) and Google Scholar/PoP, covering the period 2018–2026, focusing on the intersection of Digital Twin, Disaster Management, and Data Governance. Additionally, a qualitative case study approach was employed, utilizing Indonesia as the primary representation of developing countries to validate the proposed framework. The study identifies three key challenge dimensions: (1) Organizational (data silos and ownership ambiguity), (2) Technical (semantic interoperability and legacy systems), and (3) Legal-Ethical (data privacy and sovereignty). The paper proposes the "Integrated Disaster Data Governance for Digital Twin (IDDG-DT)" framework, which aligns with the Satu Data Indonesia policy, emphasizing that robust data governance is a prerequisite for successful Digital Twin implementation.
A Systematic Literature Review on Intelligent Tutoring Systems for Outcome-Based Education in Higher Education Hasbu Naim Syaddad; Andi Agus Salim; Luki Ishwara; Zainal Arifin Hasibuan; Bobi Kurniawan; Sri Supatmi
Technologia Journal Vol. 3 No. 1 (2026): Technologia Journal-February
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/pd6g0a26

Abstract

Penerapan Outcome-Based Education (OBE) di pendidikan tinggi menuntut pendekatan pembelajaran yang mampu mendukung pencapaian capaian pembelajaran dan kompetensi mahasiswa secara terukur. Intelligent Tutoring Systems (ITS) merupakan sistem pembelajaran berbasis kecerdasan buatan yang bersifat adaptif dan personal, sehingga berpotensi mendukung implementasi OBE. Namun, temuan empiris terkait penerapan dan efektivitas ITS dalam konteks OBE di pendidikan tinggi masih tersebar dan belum tersintesis secara sistematis. Penelitian ini bertujuan untuk mengkaji peran, karakteristik, dan efektivitas ITS dalam mendukung outcome-based education di pendidikan tinggi. Penelitian ini menggunakan metode systematic literature review dengan mengacu pada pedoman PRISMA 2020. Pencarian literatur dilakukan melalui basis data Scopus terhadap artikel jurnal berbahasa Inggris yang dipublikasikan pada periode 2018–2025. Dari proses seleksi yang ketat, sebanyak 56 artikel jurnal memenuhi kriteria inklusi dan dianalisis menggunakan pendekatan sintesis naratif. Hasil kajian menunjukkan bahwa ITS umumnya dibangun atas komponen inti berupa model peserta didik, model domain, model pedagogik, dan antarmuka tutor. Teknik kecerdasan buatan yang banyak digunakan meliputi machine learning, rule-based systems, Bayesian networks, dan natural language processing. Sebagian besar studi melaporkan bahwa ITS berdampak positif terhadap kinerja akademik, penguasaan kompetensi, dan keterlibatan mahasiswa. Meskipun demikian, penelitian lanjutan masih diperlukan untuk mengevaluasi dampak jangka panjang dan integrasi ITS dalam kerangka OBE di tingkat institusi.  
The Philosophical Foundations of Digital-Twin-Based IT Governance: A Philosophy-of-Science Perspective Andi Agus Salim; Hasbu Naim Syaddad; Luki Ishwara; Agus Nursikuwagus; Usep Muhammad Ishaq; Andrias Darmayadi
JRSI (Jurnal Rekayasa Sistem dan Industri) Vol. 13 No. 01 (2026): Jurnal Rekayasa Sistem & Industri
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The increasing adoption of digital twins in organisational settings has extended their use beyond technical optimisation toward decision support and governance functions. However, existing studies on digital twin-based IT governance largely emphasise technical architectures and performance considerations, while paying limited attention to the philosophical assumptions that shape how these systems represent reality, generate knowledge, and influence decision-making. This paper addresses this gap by examining digital twin-based IT governance through a philosophy-of-science perspective. Drawing on concepts from ontology, epistemology, and scientific modelling, this study conceptualises digital twins as socio-technical artefacts that function simultaneously as models and interventions. The analysis demonstrates that digital twins embed specific assumptions about what constitutes organisational reality, what forms of knowledge are considered valid, and how governance actions are legitimised. These assumptions have direct implications for accountability, transparency, and human agency in IT governance processes. Based on this analysis, the paper articulates design implications for digital twin-based IT governance, highlighting the importance of interpretability, human-in-the-loop mechanisms, reflexive design, and value-sensitive approaches. Rather than offering empirical generalisations, the contribution of this paper is conceptual: it provides a structured analytical framework that clarifies the foundational assumptions underpinning governance-oriented digital twins. The paper concludes by outlining directions for future empirical and design-oriented research aimed at developing digital twin-based governance systems that are not only technologically effective but also epistemically robust and socially responsible
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 Agus Nursikuwagus Andrias Darmayadi Bobi Kurniawan, Bobi Estiko Rijanto Handoko Handoko Hasbu Naim Syaddad Hasbu Naim Syaddad Hasbu Naim Syaddad Irawan Irfan Dwiguna Luki Ishwara Luki Ishwara Rio Yunanto Sri Supatmi Syaddad, Hasbu Naim Usep Muhammad Ishaq Zainal Arifin Hasibuan