Prioritas Peruntukan Ruang Terbuka Hijau Berdasarkan Distribusi Suhu Permukaan di Kota Palu
Bibliographic record
Abstract
Alih fungsi penggunaan lahan dan berkurangnya area vegetasi terjadi akibat urbanisasi yang pesat, yang secara signifikan berkontribusi pada peningkatan suhu di kawasan perkotaan (Fardani, 2022). Kehadiran RTH di kawasan perkotaan sangat erat kaitannya dengan mitigasi efek pulau panas (Urban Heat Island).Penelitian ini bertujuan untuk menganalisis faktor-faktor prioritas peruntukan Ruang Terbuka Hijau (RTH) berdasarkan distribusi suhu permukaan dan menganalisis arahan pengembangan ruang terbuka hijau di Kota Palu. Metode penelitian ini adalaha deskriptif kuantitaif dengan merumuskan suhu permukaan dan indeks kerapatan vegetasi, serta mempertimbangkan kenyamanan termal dan kepadatan penduduk. Teknik overlay berbobot digunakan untuk mengidentifikasi wilayah dengan prioritas tinggi dalam pengembangan RTH. Hail penelitian ini menujukkan sebagian besar wilayah Kota Palu tergolong non-prioritas (kelas 1–3) dengan luas 35.321,99 ha (90%). Zona prioritas pertama (kelas 5) seluas 2.544,75 ha (6,48%) dan prioritas kedua (kelas 4) 1.384,50 ha (3,53%). Empat kecamatan yaitu Palu Selatan, Tatanga, Palu Barat, dan Palu Timur menyumbang 85,3% dari total luasan prioritas. Wilayah ini memiliki suhu tinggi, kenyamanan termal rendah, vegetasi jarang, dan kepadatan penduduk tinggi. Kondisi ini menjadi dasar penting dalam perencanaan pengembangan RTH strategis. Arahan pengembangan RTH difokuskan pada integrasi penghijauan di lahan terbangun, penyediaan RTH di lahan terbuka, dan peningkatan kualitas pada RTH eksisting. Penelitian ini diharapkan dapat menjadi masukan dalam upaya mitigasi perubahan iklim dan peningkatan kualitas lingkungan perkotaan di Kota Palu. Land use conversion and the reduction of vegetated areas occur as a result of rapid urbanization, which significantly contributes to rising temperatures in urban areas (Fardani, 2022). The presence of green open spaces (GOS) in urban areas is closely related to the mitigation of the Urban Heat Island effect. This study aims to analyze the priority factors for green open space allocation based on surface temperature distribution and to examine development guidelines for green open space in the city of Palu. This research employs a descriptive quantitative method by formulating surface temperature and vegetation density index, while also considering thermal comfort and population density. A weighted overlay technique is used to identify areas with high priority for GOS development. The findings show that most areas in Palu are categorized as non-priority (classes 1–3), covering an area of 35,321.99 hectares (90%). The highest priority zone (class 5) covers 2,544.75 hectares (6.48%), while the second priority zone (class 4) covers 1,384.50 hectares (3.53%). Four districts are South Palu, Tatanga, West Palu, and East Palu contribute 85.3% of the total high-priority area. These areas are characterized by high temperatures, low thermal comfort, sparse vegetation, and high population density. These conditions serve as a critical foundation for the strategic planning of GOS development. The recommended strategies focus on integrating greenery into built-up areas, providing GOS in open lands, and improving the quality of existing green spaces. This study is expected to serve as input for climate change mitigation efforts and the improvement of urban environmental quality in Palu.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".