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p-Index From 2021 - 2026
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All Journal Media Jurnal Informatika Big Data Analytics and Data Science
Andi Agus Salim
Universitas Komputer Indonesia
Computer Science & IT

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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; 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.
Co-Authors Agus Nursikuwagus Estiko Irawan Irfan Dwiguna Luki Ishwara Sri Supatmi Syaddad, Hasbu Naim Zainal Arifin Hasibuan