Kajian Konsep Infrastruktur Hijau untuk Menurunkan Suhu Udara di Kawasan Teknopolis SWK Gedebage
Bibliographic record
Abstract
Abstract. According to the Bandung City document detail spatial planning, SWK Gedebage is designated as a Bandung City Service Center with the Technopolis concept. With the existence of these regulations, the development and construction is rapid and triggers the level of demand for land. The increase in the demand for land that occurs requires the conversion of green land into built-up land and has an impact on increasing temperatures due to development. This can affect changes in climate elements which will affect the increase in air temperature. One of the efforts that can be made to reduce air temperature is to apply the concept of green infrastructure in every development process in urban areas. Green infrastructure can help reduce urban heat. This study aims to determine the distribution of hotspots in SWK Gedebage using Landsat 8 OLI imagery and extract TOA, BT, NDVI, PVI and Emissivity values to obtain LST values and compare them to the previous 5 years. Then a simulation of the application of the green infrastructure concept was carried out using the Envi-Met software and the result was that the green wall can reduce the air temperature > 1oC while the green roof can help reduce the air temperature up to 0.89oC.
 Abstrak. Menurut RDTR Kota Bandung, SWK Gedebage diperuntukkan sebagai Pusat Pelayanan Kota Bandung dengan konsep Teknopolis. Dengan adanya peraturan tersebut maka perkembangan dan pembangunannya pesat dan memicu terhadap tingkat kebutuhan lahan. Peningkatan akan kebutuhan lahan yang terjadi menuntut adanya alih fungsi lahan hijau menjadi lahan terbangun dan berdampak kepada peningkatan suhu akibat pembangunan. Hal ini dapat berpengaruh terhadap perubahan unsur iklim yang akan berpengaruh terhadap kenaikan suhu udara. Salah satu upaya yang dapat dilakukan untuk menurunkan suhu udara adalah dengan menerapkan konsep infrastruktur hijau dalam setiap proses pembangunan di perkotaan. Infrastruktur hijau mampu membantu menurunkan suhu panas perkotaan. Penelitian ini bertujuan untuk mengetahui persebaran titik panas di SWK Gedebage menggunakan citra landsat 8 OLI dan mengekstraksi nilai TOA, BT, NDVI, PVI dan nilai Emisivitas sehingga mendapatkan nilai LST dan dibandingkan dengan 5 tahun sebelumnya. Kemudian dilakukan simulasi penerapan konsep infrastruktur hijau menggunakan software Envi-Met dan didapatkan hasil bahwa green wall dapat menurunkan suhu udara >1oC sementara green roof dapat mmebantu menurunkan suhu udara sampai dengan 0.89oC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".