APLIKASI PEMODELAN HEC-HMS UNTUK IDENTIFIKASI KEJADIAN BANJIR BANDANG DI DAS CIMANUK HULU, KABUPATEN GARUT
Bibliographic record
Abstract
Banjir adalah salah satu dari tiga bencana yang selalu terjadi di Indonesia. Banjir bandang di DAS Cimanuk Hulu merupakan bencana terbesar yang terjadi di tahun 2016. Kejadian ini menyebabkan 34 orang meninggal dunia, 19 orang hilang, dan 9 orang terluka. Penelitian ini akan mengidentifikasi subDAS priorotas penyebab banjir bandang Garut dan menganalisis faktor-faktor yang menyebabkannya. Pemodelan HEC-HMS digunakan untuk menentukan sub DAS prioritas. Analisis geomorfologi dan dan observasi lapangan dilakukan untuk menjelaskan faktor-faktor penyebab bajir bandang. Hasil penelitian menunjukkan Hulu DAS Cimanuk di Gunung Papandayan dan Cikuray merupakan penyumbang limpasan permukaan tertingi dengan nilai 39,9 m3/s/km2 dan 50,1 m3/s/km2. Faktor-faktor yang menyebabkan hal tersebut adalah curah hujan, karakteristik tanah, dan morfometri DAS. Curah hujan rerata tahunan selama lima tahun menunjukkan nilai 2941-3154 mm. Permeabilitas tanah memiliki nilai rendah yang tergolong sebagai hydrological soil group bertipe D. Kerapatan aliran dan time concentration memiliki kelas sedang hingga tinggi. Kombinasi ketiga aspek tersebut merupakan penyebab utama banjir bandang Garut.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".