Efektivitas Kolam Retensi dalam Penanganan Bencana Banjir di Kecamatan Jatinangor, Kabupaten Sumedang
Bibliographic record
Abstract
Abstract. Flood disaster is one of the disasters that often occur in Jatinangor District. Floods in the Jatinangor District have an impact on residential and agricultural areas. The government implemented the construction of a retention pond as a flood control effort in the Jatinangor District. This study aims to identify the optimization of the application of retention ponds in handling floods in the Jatinangor District. The main cause of the flood disaster in Jatinangor District is the narrowing of the Cikeruh River due to the deposition of mud and garbage. Retention ponds are able to handle planned inundation worth 4.08% at the administrative scope, 4.09% at the sub-watershed scope, and 295% at the scope of the area directly affected by the presence of retention ponds or areas within a 400 meter radius of the retention pond location. The retention pond effectively overcame inundation in the neighboring areas, so that the retention pond could evacuate the residential area consisting of 948 houses from flooding. The retention pond did not effectively alleviate flooding at the administrative and sub-watershed levels. Abstrak. Bencana banjir merupakan salah satu bencana yang cukup sering terjadi di Kecamatan Jatinangor. Banjir di Kecamatan Jatinangor berdampak pada kawasan permukiman dan pertanian. Pemerintah menerapkan pembangunan kolam retensi sebagai Upaya pengendalian banjir di Kecamatan Jatinangor. Tujuan dari penelitian ini yaitu untuk mengidentifikasi optimalisasi penerapan kolam retensi dalam penanganan banjir di Kecamatan Jatinangor. Penyebab utama bencana banjir di Kecamatan Jatinangor yaitu penyempitan Sungai Cikeruh akibat pengendapan lumpur dan sampah. Kolam retensi mampu menangani genangan rencana senilai 4,08% pada lingkup administrasi, 4,09% pada lingkup sub-DAS, dan 295% pada lingkup wilayah yang secara langsung terdampak oleh keberadaan kolam retensi atau wilayah dalam radius 400 meter dari lokasi kolam retensi. Kolam retensi secara efektif mengatasi genangan pada wilayah di dekatnya, sehingga keberadaan kolam retensi dapat mengevakuasi kawasan permukiman yang terdiri dari 948 rumah di sekitarnya dari bencana banjir. Kolam retensi tidak mengatasi banjir secara efektif pada lingkup administrasi dan sub-DAS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".