RISIKO BENCANA TANAH LONGSOR TERHADAP PEMANFAATAN RUANG DI KECAMATAN SUKAMAKMUR KABUPATEN BOGOR
Bibliographic record
Abstract
Rencana Tata Ruang Kabupaten Bogor tahun 2016-2036 memiliki 22 (duapuluh dua) Kecamatan yang di tetapkan sebagai kawasan rawan bencana longsor salah satunya Kecamatan Sukamakmur. Tujuan dari penelitian ini adalah mengidentifikasi tingkat ancaman longsor, mengidentifikasi tingkat kerentanan longsor, mengidentifikasi tingkat kapasitas daerah, mengidentifikasi risiko bencana longsor serta mengidentifikasi risiko bencana longsor terhadap pemanfaatan ruang di Kecamatan Sukamakmur. Metode penelitian yang digunakan yaitu metode analisa spatial (GIS) dengan cara overlay, pembobotan dan skoring dan metode analisa deskriptif kuantitatif. Berdasarkan hasil analisa Kecamatan Sukamkmur memiliki ancaman longsor rendah 22,44%, sedang 56,21 % dan tinggi 21,28%. Kerentanan longsor terdiri dari kerentanan rendah 60,67% dan sedang 39,24%. Kapasitas daerah terdiri dari kapasitas daerah sedang 68,80% dan tinggi 31,19%. Selanjutnya untuk Risiko bencana longsor di Kecamatan Sukamakmur memiliki Risiko rendah 56,83 %, risiko sedang 40,84 % risiko tinggi 2,33 %. Analisa Risiko bencana longsor terhadap Pemanfaatan Ruang (RTRW) Kabupaten Bogor tahun 2016-2036 yang berada di Kecamatan Sukamakmur didominasi oleh kawasan permakiman perkotaan kepadatan rendah dengan luas 3793 Ha memiliki tingkat risiko rendah sebesar 29,91 %, tingkat risiko sedang 11,87%, tingkat risiko tinggi sebesar 19,78%.Kata kunci : Pemanfaatan Ruang, Risiko Longsor
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".