Mitigasi Banjir Berbasis Tingkat Kerawanan Banjir di Kecamatan Rambipuji, Kabupaten Jember
Bibliographic record
Abstract
Kecamatan Rambipuji merupakan salah satu kecamatan yang berdasarkan RPJMD Kabupaten Jember Tahun 2021-2026 dan data historis Badan Nasional Penanggulangan Bencana Daerah termasuk wilayah rawan banjir yang disebabkan curah hujan ekstrim, kondisi geografis, dan kurangnya sistem informasi kebencanaan. Pada kisaran tahun 2005 dan 2006, banjir menyebabkan kerugian materi hingga nyawa berdasarkan data BPBD Kabupaten Jember. Penelitian ini bertujuan menganalisis kerawanan banjir berdasarkan tingkatannya dan mengidentifikasi faktor-faktor penyebab kerawanan banjir untuk perumusan strategi mitigasi efektif. Metode yang digunakan meliputi pendekatan geospasial dengan Sistem Informasi Geografis (SIG), analisis Weight of Evidence (WoE) untuk menentukan faktor utama penyebab banjir, serta analisis SWOT dan IFAS EFAS untuk perumusan strategi mitigasi. Hasil penelitian menunjukkan bahwa kerawanan banjir di Kecamatan Rambipuji terklasifikasi pada kelas sangat rendah hingga sangat tinggi dimana faktor penggunaan lahan sebagai penyebab utama. Dirumuskan strategi mitigasi yang mencakup pembangunan infrastruktur pengendali banjir, peningkatan sistem peringatan dini, serta edukasi masyarakat mengenai kesiapsiagaan bencana. Output penelitian diharapkan menjadi sumbangsih sebagai upaya pengurangan resiko bencana banjir dan peningkatan kesiapsiagaan dalam menghadapi potensi bencana
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 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".