Percentage of Appearance of Physical Condition Applications for Badminton Athletes Aged 10-12 Years Old Based on Android Smartphone
Bibliographic record
Abstract
Badminton games can run well, mastery of techniques or basic game skills is needed. Badminton players must also have good physical abilities. There needs to be an application that supports the performance of the coach. The purpose of creating a product is to realize an attractive design for coaches, especially badminton. The purpose of this study was to determine the percentage of physical condition application displays for badminton athletes aged 10-12 years based on android smartphones. This study uses a survey-based quantitative descriptive method. The population of this study consisted of coaches at badminton clubs in Boyolali District. The sample in this study amounted to 15 coaches at the badminton club in Boyolali Regency were taken using a purposive sampling technique. Data collection techniques in this study used a questionnaire instrument with a Likert scale. Data analysis used SPSS version 25. The results showed that the percentage of physical condition application displays for badminton athletes aged 10-12 years based on Android smartphones in the very good category obtained a percentage of 93.33% with a total of 14 coaches and a good percentage of 6.67 % with a total of 1 coach. So it can be concluded that this application has a very good appearance in the view of the badminton coach.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".