Perencanaan Sebaran Lokasi Ruang Terbuka Hijau Publik Kelurahan Sukarame
Bibliographic record
Abstract
Abstract. The availability of public green open space (RTHP) plays a vital role in maintaining environmental quality and promoting social well-being in urban areas. However, in Sukarame Subdistrict, Bandar Lampung City, the current provision of RTHP accounts for only 0.19% of the total area, which falls significantly below the national standard of 20%. This article aims to analyze the need for RTHP based on land area and population, and to identify potential locations for future development. The study applies a quantitative-spatial approach utilizing secondary data analysis, satellite image interpretation, and GIS-based spatial mapping through Google Earth and ArcGIS. Key aspects analyzed include existing land use, land ownership status from the BhumiATR platform, and physical land conditions. The results indicate that the ideal requirement of RTHP ranges from 44.9 to 53.2 hectares, while the identified potential land area only reaches 1.07 hectares across nine sites. The dominance of privately owned land presents challenges in the allocation of new RTHP areas. Therefore, the study recommends an integrated land acquisition strategy prioritizing government-owned land and promoting collaborative, adaptive planning to support sustainable urban environments. Abstrak. Ketersediaan ruang terbuka hijau publik (RTHP) memiliki peran yang penting dalam menjaga kualitas lingkungan dan kehidupan sosial masyarakat perkotaan. Namun, di Kelurahan Sukarame, Kota Bandar Lampung, luasan RTHP yang tersedia hanya mencapai 0,19% dari total wilayah, jauh di bawah standar nasional sebesar 20%. Artikel ini bertujuan untuk menganalisis kebutuhan RTHP berdasarkan luas wilayah dan jumlah penduduk, serta mengidentifikasi lokasi potensial penyediaan RTHP yang layak dikembangkan. Pendekatan analisis yang digunakan mencakup pengolahan data sekunder, interpretasi citra satelit, serta pemetaan spasial berbasis perangkat lunak Google Earth dan ArcGIS. Aspek-aspek yang dianalisis meliputi penggunaan lahan eksisting, status kepemilikan lahan dari platform Bhumi ATR, serta kondisi fisik lahan. Hasil studi menunjukkan bahwa kebutuhan ideal RTHP di Kelurahan Sukarame mencapai 44,9–53,2 hektar, sementara lahan potensial yang berhasil diidentifikasi hanya seluas 1,07 hektar yang tersebar di sembilan lokasi. Dominasi kepemilikan hak milik pribadi menjadi tantangan dalam perencanaan pengadaan lahan RTHP. Oleh karena itu, disarankan adanya integrasi kebijakan pengadaan lahan RTHP dengan prioritas pada lahan milik pemerintah dan pendekatan perencanaan yang kolaboratif dan adaptif dalam rangka mendukung keberlanjutan lingkungan dan sosial perkotaan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".