Designing a Sustainable ICT Infrastructure Framework for Nusantara: Enabling Smart City Governance and Environmental Resilience in Indonesia’s New Capital
Bibliographic record
Abstract
This study presents a sustainable ICT infrastructure framework to support the development of Nusantara, Indonesia's new capital city, in alignment with smart and green city principles.The objective is to enable efficient urban management, environmental sustainability, and datadriven governance through technological integration and multi-level coordination.The proposed framework is structured into four levels: prerequisites, enabling technologies, strategic domains, and outcome indicators.It incorporates advanced technologies such as AI, IoT, big data analytics, and digital twins to optimize resource use, energy efficiency, and service delivery.The research methodology includes literature review, document analysis, conceptual modelling, and framework validation.Validation was conducted through alignment with the ICT ecosystem, the Smart City Management System, and the official Nusantara smart city development blueprint.Results demonstrate the framework's scalability, adaptability, and alignment with sustainability and governance goals.It supports hierarchical decision-making, real-time monitoring, and phased implementation.The study concludes with recommendations for further research, including pilot projects, network optimisation, and the creation of a virtual capital city using digital twin technology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".