Analisis Banjir pada Polder Sunter Timur II dengan Menggunakan HEC-RAS (Ras Mapper)
Bibliographic record
Abstract
Abstrak Banjir di Jakarta menimbulkan kerusakan dan kerugian, menjadi latar belakang dilakukannya analisis banjir di Polder Sunter Timur II yang disajikan dalam makalah ini. Wilayah hilir Jakarta terbagi menjadi 43 sistem polder dan salah satunya adalah Polder Sunter Timur II. Studi ini bertujuan untuk mendapatkan luas daerah banjir rencana Polder Sunter Timur II. Analisis hidrologi menggunakan software HEC-HMS dan analisis banjir menggunakan HEC-RAS (Ras Mapper). Data input yang digunakan dalam pemodelan yaitu Digital Elevation Model (DEM), hidrograf, peta penggunaan lahan. Daerah tangkapan air polder Sunter Timur II sebesar 1,328 km2. Hasil simulasi HEC-HMS didapat debit banjir rencana kala ulang 5, 10 dan 25 tahun yaitu sebesar 185 m3/dt, 208,7 m3/dt, 234,3 m3/dt. Hasil simulasi HEC-RAS menunjukkan luas daerah banjir dengan debit banjir kala ulang 5, 10 dan 25 tahun adalah 857,08 Ha, 885,62 Ha, 979,59 Ha. Kata kunci: banjir, sistem polder, analisis hidrologi, HEC-HMS, HEC-RAS Abstract Flood in Jakarta cause damage and losses are the reason for flood analysis of the East Sunter II Polder presented in this paper. Downstream area of Jakarta is divided into 43 polder systems and one of them is East Sunter II Polder. This study aims to obtain the flood area of Polder East Sunter II plan. Hydrological analysis using HEC-HMS software and flood analysis using HEC-RAS (Ras Mapper). Input data used in modeling are Digital Elevation Model (DEM), hydrograph, land use map. Catchment area of the East Sunter II polder is 1,328 km2. The result of HEC-HMS, for 5-, 10- and 25-year return period flood discharge is 185 m3/s, 208,7 m3/s, 234 m3/s. The results of HEC-RAS show that the flood area with 5, 10 and 25-year return period flood discharge is 857,08 Ha, 885,62 Ha, 979,59 Ha. Keywords: flood, polder system, hydrology analysis, HEC-HMS, HEC-RAS
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".