POTENSI RAWAN BANJIR KECAMATAN MUARA BANGKAHULU SEBAGAI PENUNJANG PEMBELAJARAN MATERI PEMANASAN GLOBAL DI SMPN 11 KOTA BENGKULU
Bibliographic record
Abstract
Abstrak Dalam mencapai tujuan penelitian, pemetaan wilayah potensi banjir melalui pendekatan zonasi spasial berdasarkan elevasi, jarak dari sungai, dan jarak dari garis pantai. Data koordinat dan elevasi diperoleh langsung di lapangan menggunakan GPS Epoch TM 10 L1. Hasil penelitian ditampilkan sebagai penunjang dalam pembelajaran Pemanasan Global melalui pembelajaran berbasis masalah (PBL). Penelitian ini menggunakan metode quasi eksperimen dengan 32 peserta didik. Hasil penelitian didapat peta rawan banjir yang diklasifikasikan ke dalam 3 zona rawan banjir, yaitu zona rawan satu, zona rawan dua, dan zona rawan tiga dengan wilayah paling rawan ada pada Kelurahan Rawa Makmur, Rawa Makmur Permai, dan Beringin Raya. Implementasi penelitian pada pembelajaran menunjukkan bahwa setelah belajar menggunakan model Problem Based Learning (PBL): 1) Hasil belajar kognitif peserta didik mengalami peningkatan dengan nilai rata-rata N-gain kelompok tinggi 0,81 (kriteria tinggi), kelompok sedang 0,60 (kriteria sedang), dan kelompok rendah 0,46 (kriteria sedang); 2) Terdapat perbedaan hasil belajar kognitif peserta didik antara kelompok tinggi, sedang dan rendah berdasarkan hasil Uji-Anava dengan nilai Fhitung = 20,68 lebih besar dari Ftabel = 3,33 pada taraf signifikansi 5%. Kata kunci: Kecamatan Muara Bangkahulu, Banjir, Rawan, Pemanasan Global, Model Problem Based Learning (PBL), Hasil belajar kognitif.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.013 |
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".