Identifikasi Suhu Panas Permukaan Pada Bagian Wilayah Kota 1 Semarang
Bibliographic record
Abstract
Abstract. Changes in land use in urban areas in Semarang aim to provide facilities and infrastructure that can increase the existing economic level, this results in a reduction in open land which can increase surface heat temperatures in urban areas. This study aims to identify changes in land surface temperature (LST) in the Sub-Region of Semarang City (BWK I) between 2020 and 2025. A quantitative approach was applied using spatial analysis through Landsat 8 satellite imagery and processed using ArcGIS with seven analytical stages. The results show a significant increase in surface temperature. Areas with low (<30.7°C) and moderate (30.7–32.7°C) temperatures decreased by 87.857 ha and 555.400 ha, respectively. Conversely, high-temperature areas (32.7–34.7°C) increased by 744.154 ha, and very high-temperature zones (>34.7°C) emerged in South Semarang, covering 4.313 ha. This temperature rise is primarily due to land use changes from vegetated to built-up areas with minimal green space. These findings reflect an urban microclimate warming trend, indicating environmental degradation. Therefore, mitigation efforts are necessary, including increasing vegetation cover, developing green open spaces, and controlling land use conversion to maintain urban ecological balance and slow the rise in surface temperatures. Abstrak. Perubahan fungsi lahan pada Kawasan perkotaan di Semarang bertujuan untuk memenuhi sarana dan prasarana yang bisa meningkatkan Tingkat perekonomian yang ada, hal ini mengakibatkan berkurangya lahan terbuka yang dapat meningkatkan suhu panas permukaan pada Kawasan perkotaan. Penelitian ini bertujuan untuk mengidentifikasi perubahan suhu panas permukaan di Bagian Wilayah Kota (BWK) 1 Semarang antara tahun 2020 dan 2025. Metode yang digunakan adalah pendekatan kuantitatif melalui analisis spasial menggunakan citra satelit Landsat 8 dengan bantuan aplikasi ArcGIS melalui tujuh tahapan pengolahan data. Hasil analisis menunjukkan adanya peningkatan signifikan pada suhu permukaan. Wilayah bersuhu rendah (<30,7°C) dan sedang (30,7–32,7°C) mengalami penurunan luas masing-masing sebesar 87,857 ha dan 555,400 ha. Sebaliknya, terjadi peningkatan luas pada suhu tinggi (32,7–34,7°C) sebesar 744,154 ha, dan munculnya wilayah bersuhu sangat tinggi (>34,7°C) seluas 4,313 ha di Semarang Selatan. Faktor utama peningkatan ini adalah perubahan penggunaan lahan dari vegetasi menjadi area terbangun yang minim ruang terbuka hijau. Kondisi ini menunjukkan tren pemanasan mikroklimat perkotaan yang mengarah pada penurunan kualitas lingkungan. Oleh karena itu, dibutuhkan upaya mitigasi melalui peningkatan tutupan vegetasi, pengembangan ruang terbuka hijau, serta pengendalian alih fungsi lahan guna menjaga keseimbangan ekosistem kota dan menghambat laju peningkatan suhu permukaan.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".