Analisis Kesiapsiagaan Komunitas Sekolah Muhammadiyah dalam Menghadapi Bencana Tanah Longsor di Kabupaten Karanganyar
Bibliographic record
Abstract
Kabupaten Karanganyar merupakan suatu wilayah yang memiliki potensi terhadap bencana tanah longsor. Tujuan penelitian ini adalah untuk mengetahui kesiapsiagaan di SD, SMP dan SMA Muhammadiyah terhadap bencana tanah longsor di Kabupaten Karanganyar, serta mengetahui perbandingan kesiapsiagaan di SD, SMP dan SMA Muhammadiyah terhadap bencana tanah longsor di Kabupaten Karanganyar. Pengambilan sampel dilakukan dengan menggunakan teknik stratified random sampling dimana sampel yang dipilih berdasarkan strata atau tingkatan. Metode analisis data menggunakan analisis deskriptif kualitatif, dengan menggunakan pedoman kesiapsiagaan yang bersumber dari LIPI 2006. Hasil dari penelitian ini menunjukkan bahwa kesiapsiagaan Komunitas Sekolah Muhammadiyah dalam menghadapi bencana tanah longsor sangatlah beragam. Kesiapsiagaan siswa Muhammadiyah memiliki kategori sangat siap dalam menghadapi bencana tanah longsor, sementara itu kesiapsiagaan guru memiliki kategori siap dalam menghadapi bencana tanah longsor, akan tetapi kesiapsiagaan kepala sekolah yakni termasuk kedalam belum siap dalam menghadapi bencana tanah longsor. Sementara itu tingkat perbandingan kesiapsiagaan siswa, guru, dan kepala sekolah memiliki perbandingan yang sangat signifikan, yakni berdasarkan hasil yang telah diperoleh dapat dijelaskan bahwa kesiapsiagaan siswa dan guru Muhammadiyah sangatlah tinggi jika dibandingkan dengan kepala sekolah yang belum siap dalam menghadapi bencana tanah longsor berdasarkan parameter-parameter yang telah ditentukan.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.013 | 0.002 |
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".