PERAN RUANG TERBUKA HIJAU DALAM PERENCANAAN KOTA SEBAGAI POTENSI PEMBENTUK SMART CITY
Bibliographic record
Abstract
Ruang terbuka hijau telah dikenal memiliki peranan yang penting dalam meningkatkan kualitas lingkungan perkotaan. Ruang terbuka hijau dalam perencanaan kota kerap dianggap sebagai elemen pendukung terwujudnya smart city. Penelitian ini bertujuan untuk menemukan seberapa jauh peran ruang terbuka hijau lewat penyediaan wifi corner dalam perencanaan kota menuju konsep smart city. Metode yang digunakan adalah deskriptif kualitatif berdasarkan observasi, survey lapangan dan serangkaian wawancara. Studi kasus yang diambil dalam penelitian ini adalah dua kota di Kalimantan Selatan, yaitu Banjarmasin dan Banjarbaru yang telah memiliki konsep smart city. Hasil penelitian menunjukkan bahwa ruang terbuka hijau dalam perencanaan kota memiliki potensi yang kuat sebagai elemen pembentuk smart city. Apabila ruang terbuka hijau suatu kota telah direncanakan dengan baik dari berbagai segi fasilitas dan terkoneksi dengan jaringan internet yang berkualitas, maka dengan sendirinya konsep smart city akan lebih mudah dicapai. Kata kunci: perencanaan kota, ruang terbuka hijau, smart city, wifi corner. Green space has an important role in enhancing environmental quality of a city. Green space often considered as a supporting element for the concept of smart city. This research intended to acknowledge the role of green space through the installation of wifi corner in urban planning towards smart city. The methods that has been used was descriptive qualitative through observation, field survey and interviews. The case study in this research were Banjarmasin and Banjarbaru which already has the smart city concepts. The result shows that green space in urban planning is a potential element towards smart city. A well good planned green space with all the facilities that connected to a good internet network in a city might help forming the concept of smart city. Keywords: green space, smart city, urban planning, wifi corner.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.019 |
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".