Management Strategy for Public Green Open Spaces in Medan City Using SWOT Analysis
Bibliographic record
Abstract
Medan City, the third-largest city in Indonesia, faces significant challenges in managing its public green open spaces (RTH) due to high population density, rapid urbanization, and insufficient green space, which falls far short of the 30% mandated by law. This research assesses the management of public green spaces in Medan through a SWOT analysis, revealing internal strengths such as a Regional Spatial Plan and government commitment, alongside weaknesses like suboptimal management, lack of coordination between agencies, and inadequate regulations. External factors, including opportunities from NGO funding and potential land acquisition, contrast with threats like rapid population growth and misuse of green spaces. Through data collection methods, including focus group discussions, questionnaires, and interviews with key stakeholders, the research identified key areas for improvement in green space management. Strategic recommendations include strengthening policies, increasing public awareness, optimizing cross-sector collaboration, and promoting sustainable urban planning. Additionally, leveraging green spaces for economic growth through multifunctional uses can enhance their value to the community. This study concludes that public green open spaces in Medan City can contribute significantly to sustainable urban development and environmental balance if managed more effectively, with better coordination, stronger regulations, and resource allocation. The findings aim to provide valuable insights for urban planners and policy makers in achieving urban sustainability goals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".