Pengaruh Model Permainan Lompat Banner Adi Terhadap Hasil Belajar Lompat Jauh Siswa Kelas II SD N 2 Puding Besar
Bibliographic record
Abstract
Tujuan penelitian ini untuk mengetahui pengaruh model permainan lompat banner ADI terhadap hasil belajar lompat jauh siswa kelas 2 SDN 2 Puding Besar. Metode penelitian yang digunakan adalah metode eksperimen murni (true-eksperimental research). Dalam penelitian ini yang menjadi populasi adalah seluruh siswa kelas II SDN 2 Puding Besar yang berjumlah 40 siswa. Teknik pengambilan sampel menggunakan teknik purposive sampling dan diperoleh sebanyak 20 siswa yang akan menjadi kelompok eksperimen yang di beri perlakuan berupa model permainan lompat banner ADI. Setelah diberikan perlakuan selama 4 kali pertemuan ternyata kelompok eksperimen mengalami peningkatan yang signifikan. Berdasarkan Analisa data uji hipotesis didapat selisih mean = 4.9 menunjukan selisih dari pretest dan posttest dan hasil t-hitung = 4.388 sedangkan t-tabel = 2.093 artinya t-hitung ˃ t-tabel (4.388 ˃ 2.093), df = 19 dan p-value = 0.00 < 0.05 yang berarti terdapat pengaruh yang signifikan antara sebelum dan sesudah adanya perlakuan model pembelajaran lompat banner ADI berbasis permainan. Jadi, hipotesis yang menyatakan bahwa “ada pengaruh model permainan lompat Banner ADI terhadap hasil belajar lompat jauh pada siswa kelas II SD Negeri 2 Puding Besar”. Terbukti. Jadi kesimpulannya model permainan lompat Banner ADI dapat meningkatkan hasil belajar lompat jauh pada siswa kelas II SDN 2 Puding Besar.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".