Persebaran Fenomena Urban Heat Island di Kota Tasikmalaya Menggunakan Penginderaan Jauh
Bibliographic record
Abstract
Abstract. Tasikmalaya City is one of the major cities in West Java. In previous research there was a discussion of the Urban Heat Island phenomenon in West Java from 1989 to 2021, Tasikmalaya City was included in the location of the previous research. This research aims to map the wide distribution of the Urban Heat Island (UHI) phenomenon that occurred in Tasikmalaya City during the 2014-2023 period. The research uses quantitative methods with remote sensing techniques, utilizing Landsat 8 imagery through Google Earth Engine. It can be seen that the surface temperature from 2014 - 2023 has increased significantly, from an average of 23.55 ℃ in 2014 to 28.04 ℃ in 2023. Then there is an expansion of the Urban Heat Island phenomenon in Tasikmalaya City. The Urban Heat Island area in Tasikmalaya City increased by 7.67 km², from 50.25 km² in 2014 to 57.92 km² in 2023. The extent of the Urban Heat Island phenomenon increases over time, due to the lack of vegetation or vegetated land with a fairly dense building density and an increase in the rate of population growth in Tasikmalaya City. Abstrak. Kota Tasikmalaya salah satu kota besar yang terdapat di Jawa Barat. Pada penelitian terdahulu terdapat pembahasan mengenai fenomena Urban Heat Island di Jawa Barat tahun 1989 sampai 2021, Kota Tasikmalaya termasuk dalam lokasi penelitian terdahulu tersebut. Penelitian ini bertujuan untuk memetakan sebaran luas fenomena Urban Heat Island (UHI) yang terjadi di Kota Tasikmalaya selama periode 2014-2023. Penelitian menggunakan metode kuantitatif dengan teknik penginderaan jauh, memanfaatkan citra Landsat 8 melalui Google Earth Engine. Dapat diketahui suhu permukaan dari tahun 2014 - 2023 mengalami kenaikan yang signifikan, dari rata-rata 23,55℃ pada tahun 2014 menjadi 28,04℃ pada tahun 2023. Lalu adanya perluasan fenomena Urban Heat Island di Kota Tasikmalaya. Luas Urban Heat Island di Kota Tasikmalaya meningkat sebesar 7,67 km², dari 50,25 km² pada tahun 2014 menjadi 57,92 km² pada tahun 2023. Luasan fenomena Urban Heat Island ini seiring berjalannya waktu bertambah luas, disebabkan kurangnya vegetasi maupun lahan vegetasi dengan kerapatan bangunan yang cukup padat dan adanya kenaikan laju pertumbuhan penduduk di Kota Tasikmalaya.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".