IDENTIFIKASI POTENSI DAN MANAJEMEN PENCEGAHAN BENCANA INDUSTRI DI KOTA CILEGON PROVINSI BANTEN
Bibliographic record
Abstract
Industrialisasi memiliki potensi yang besar dalam penerimaan PAD dan penyerapan tenaga kerja. Namun disatu sisi industri menyimpan potensi bencana, yang dapat mengancam keselamatan dan kesehatan masyarakat dan kerusakan lingkungan atau ekosistem. Penelitian ini bertujuan untuk mengidentifikasi bencana yang ditimbulkan oleh industri di Kota Cilegon dan menganalisis dan mendeskripsikan pelaksanaan manajemen pencegahan bencana industri di Kota Cilegon. Metode yang digunakan dalam penelitian ini adalah kualitatif . Data diperoleh dari hasil wawancara dan dokumentasi. Hasil penelitian menunjukkan bahwa potensi bencana industri berbeda-beda berdasarkan bidang usaha industri. Industri terbesar di Kota Cilegon adalah industri kimia (36%), sehingga potensi bencana industri terbesar adalah berasal dari industri kimia. Potensi bencana industri kimia dapat disebabkan oleh kegagalan industri seperti kebocoran zat kimia, infra struktur industri, meledaknya tabung reaktor, kebocoran gas, kebakaran, keracunan, radiasi, dan epidemi. Selain itu bencana industri disebabkan oleh bencana alam, seperti tsunami, gempa bumi, gunung meletus. Manajemen bencana untuk mencegah bencana industri di Kota Cilegon dilakukan secara terpadu oleh Dinas Lingkungan Hidup, Badan Penanggulangan Bencana Daerah dan pihak perusahaan pemilik industri. Manajemen bencana di Kota Cilegon meliputi mitigasi bencana, kesiapsiagaan, respon/daya tanggap dan pemulihan/recovery. Kata Kunci : Bencana , Industri, Manajemen
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.016 | 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".