Peningkatan Kompetensi Mitigasi Bencana Siswa Dengan Implementasi Pendekatan Bencana Dalam Perspektif Islam
Bibliographic record
Abstract
To prepare a community that is ready and alert to natural disasters needs to be instilled and introduced early to the students in the context of education how Islam sees disaster. During this time, students are of the view that Islamic values, have not touched on disaster and disaster mitigation because of their ignorance about the teachings of Islam itself. Usually they also know superficially, without going through an in-depth study of the Islamic view of the damage to the environment and the disaster itself. For that, it needs to be introduced in more depth about the Islamic view of disaster. The approach of this research is quantitative research with comparative design. The subject of this research is the students of State Junior High School located in Bangkalan District, with stratification of city, middle, and periphery schools. In this study used questionnaire method to know the initial description of students' knowledge about disaster theology approach in Islamic perspective and to know the competence of disaster mitigation attitude and skill aspects. Data analysis in this research is used T Test Indenpendent Samples Test with SPSS 20. The result of this research shows that most students have good knowledge about disaster and disaster mitigation in Islamic perspective. There is a difference in disaster mitigation competencies in attitude and skill aspects between the implementation of disaster theology approach in Islamic perspective with material-based approach to students of State Junior High School (SMPN) in Bangkalan District.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 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".