Analisis Kebutuhan Sarana Dan Prasarana Pemadam Kebakaran Di Kabupaten Samosir
Bibliographic record
Abstract
Seiring dengan penetapan Danau Toba menjadi Kawasan Strategis Pariwisata Nasional (KSPN) melalui Peraturan Presiden Nomor 18 Tahun 2020, Kabupaten Samosir mulai berbenah diri dengan mulainya pembangunan-pembangunan yang masif. Pesatnya perkembangan fisik kota memerlukan rencana dalam pengendalian dari berbagai aspek termasuk perencanaan infrastruktur kebakaran wilayah. Penanganan kebakaran di Kabupaten Samosir masih menghadapi berbagai kendala, diantaranya. terlambatnya informasi yang diterima pihak terkait pada saat kejadian kebakaran, jarak tempuh ke lokasi yang jauh, padatnya permukiman serta sistem operasional dan kelengkapan sarana prasarana pemadam kebakaran yang terbatas. Penelitian ini bertujuan untuk mengetahui jumlah dan sebaran sarana prasarana pemadam kebakaran yang dibutuhkan di Kabupaten Samosir. Data dianalisisn menggunakan metode kuantitatif dengan pendekatan spasial. Penentuan sarana dan prasarana menggunakan analisis spasial, menggunakan alat analisis arcgis teknik overlay dan buffering untuk mengidentifikasi hubungan antara suatu titik dengan area sekitar. Hasil penelitian menunjukkan bahwa diperlukan penambahan sebanyak 11 pos sektor dan 5 pos pemadam kebakaran, dimana masing-masing pos sektor membutuhkan 2 unit kendaraan pemadam kebakaran dan setiap pos membutuhkan 1 unit kendaraan pemadam kebakaran kapasitas 4000 liter, dan setiap pos membutuhkan personil sebanyak 2 regu (12 orang) serta hydran sebanyak 181 unit
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.010 |
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".