PERSEPSI SISWA TERHADAP PROGRAM SEKOLAH AMAN BENCANA (SAB) DALAM UPAYA MENINGKATKAN KESIAPSIAGAAN MENGHADAPI BENCANA DI SMP N 2 TABANAN TAHUN 2016
Bibliographic record
Abstract
Indonesia is a country that is very vulnerable to disasters. World Bank data in 2010 also states that Indonesia has a number of schools located in the fourth largest disaster prone area in the world. Therefore, the government through the National Disaster Management Agency then coordinated with the Regional Disaster Management Agency to carry out a program that had the objective of increasing disaster preparedness from the school community called the Disaster Safe School program (SAB). Preparedness is one part of the disaster management process that has an important element in controlling disaster risk. The purpose of this study is to find out an overview of students' perceptions of the Disaster Safe School program at Tabanan 2 Junior High School. This study uses a qualitative descriptive research design with phenomenological design. This research was conducted at Tabanan 2 Junior High School, located in Kediri, Tabanan. The method of data collection in this study used the FGD (focus group discussion) method and in-depth interviews. Participants in this FGD were students and students of Tabanan 2 Junior High School grouped by sex and who became informants in in-depth interviews were school teachers and SAB program holders at BPBD Bali Province. The results of the study were that students had good knowledge of knowledge about the Safe Disaster School program, students also had a good perception of the Safe Disaster School program and students had a positive attitude or supported the existence of a Disaster Safe School program
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".