DAYA LEDAK OTOT LENGAN DAN KOORDINASI MATA TANGAN TERHADAP KETEPATAN SERVIS ATAS BOLAVOLI
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui hubungan daya ledak otot lengan dan koordinasi mata-tangan dengan ketepatan servis atas. Jenis penelitian adalah korelasional. Populasi dalam penelitian ini seluruh anggota tim bolavoli Klub Kecamatan Lubuk Sikaping Kabupaten Pasaman yang aktif mengikuti latihan berjumlah 20 orang. Teknik pengambilan sampel menggunakan total sampling. Untuk pengambilan data daya ledak otot lengan menggunakan tes medicine ball put dan koordinasi mata-tangan dilakukan dengan tes ballwerfen und-fangen test (lempar tangkap bola ke dinding). Sedangkan tes Ketepatan servis atas menggunakan tes ketepatan servis atas. Data dianalisis dengan korelasi product moment dan dilanjutkan dengan korelasi ganda. Hasil penelitian menunjukkan bahwa: 1) daya ledak otot lengan mempunyai hubungan secara signifikan dengan ketepatan servis atas pemain bolavoli dan diterima kebenarannya secara empiris, serta berkontribusi sebesar 19,98%. 2) koordinasi mata-tangan mempunyai hubungan secara signifikan dengan ketepatan servis atas pemain bolavoli dan diterima kebenarannya secara empiris, serta berkontribusi sebesar 23,23%. Dan 3) daya ledak otot lengan dan koordinasimata-tangan secara bersama-sama mempunyai hubungan secara signifikan dengan ketepatan servis atas pemain bolavoli dan diterima kebenarannya secara empiris, serta berkontribusi sebesar 32,49%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".