PERAN PRCPB YONZIPUR 9/LLB/K PADA MASA TANGGAP DARURAT BENCANA GEMPA BUMI CIANJUR MELALUI PENERAPAN SISTEM INFORMASI GEOGRAFIS
Bibliographic record
Abstract
Tingginya intensitas bencana alam akhir-akhir ini, diperlukan peran pemimpin lapangan yang sigap dan responsif dalam mengatasi kendala di lapangan. Teknologi sumber informasi dan sistem pengolahan data secara cepat dan akurat memiliki peran penting dalam pengendalian situasi dan pengambilan keputusan. Yonzipur 9/LLB memiliki pasukan reaksi cepat penanggulangan bencana (PRCPB) dengan Komandan Peleton sebagai unsur pimpinan di lapangan. Pemanfaatan Teknologi Informasi pada Sistem Informasi Geografis (SIG) oleh satuan, menjadi langkah strategis bagi Komandan Peleton dalam memaksimalkan kekuatan dan menekan kerugian yang ditimbulkan. Penelitian kualitatif dengan pendekatan studi kasus digunakan guna membahas kendala, peran Komandan Peleton, dan peran SIG dalam proses penanggulangan bencana alam (lingkup peleton). Hasil penelitian berupa kendala yang dihadapi yaitu koordinasi antar instansi kurang terintegrasi, kondisi medan paska bencana yang beragam dan menyulitkan, khususnya dalam penyaluran logistik. Adanya ragam medan dan menyulitkan, Komandan Peleton berkoordinasi dengan berbagai pihak melalui alat komunikasi yang ada demi kelancaran tugas guna menjaga kondisi fisik dan moril anggotanya. Penerapan SIG berupa penentuan area dan persebaran posko dan tempat pengungsian, menentukan rute evakuasi dan distribusi logistik paling aman.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.072 | 0.048 |
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".