Efek Kerja HIIT (High Intensity Interval Training) Treadmill selama 30 Menit terhadap Kehilangan Cairan Tubuh pada Member Pria MAG (Muscle Academy Gym)
Bibliographic record
Abstract
EFEK KERJA HIIT (HIGH INTENSITY INTERVAL TRAINING) TREADMILL SELAMA 30 MENIT TERHADAP KEHILANGAN CAIRAN TUBUH PADA MEMBER PRIA MAG (MUSCLE ACADEMY GYM) Desmon Trisandi1, Eko Juli Fitrianto2, Yasep Setiakarnawijaya 2 1Program Studi Ilmu Keolahragaan 2Fakultas Ilmu Keolahragaan Universitas Negeri Jakarta, Kampus B, Jakarta Abstrak: Penelitian ini dilaksanakan di Muscle Academy Gym FIK UNJ. Metode yang digunakan ialah eksperimen dengan menggunakan desain Pre-Test dan Post-Test. Sampel yang digunakan berjumlah 15 orang dari 60 populasi member pria Muscle Academy Gym yang aktif. Disampaikan tentang nilai rata-rata (x) dari hasil yang didapatkan pada tes awal 64,66 kg dan pada tes akhir 64,08 kg. Dari hasil analisis data diperoleh selisih rata-rata (MD) 0,58 dengan standar deviasi perbedaan (SDD) 0,123 standar error perbedaan rata-rata (SEMD) 0,032. Dalam perhitungan selanjutnya diperoleh nilai t-hitung sebesar 18,12 dan t tabel 2,14 pada taraf signifikan 5% dengan demikian nilai t hitung > t tabel yang menunjukkan bahwa hipotesis nihil (Ho) ditolak dan hipotesis alternatif (H1) diterima. Maka didapat kesimpulan bahwa terdapat pengaruh aktivitas kerja HIIT Treadmill selama 30 menit terhadap kehilangan cairan tubuh pada member pria Muscle Academy Gym. Kata Kunci : High Intensity Interval Training, Treadmill, Cairan Tubuh.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
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".