SWOT ANALYSIS OF THE INDONESIAN MEN'S NATIONAL FUTSAL TEAM MATCH IN THE 2022 ASIAN CUP QUARTER-FINAL MATC
Bibliographic record
Abstract
The use of the SWOT (strength, weakness, opportunity, and threats) method is important in analyzing the factors that can cause failure. This method focuses on identifying the strengths, weaknesses, opportunities and threats of the Indonesian Futsal National Team in the quarter-finals of the 2022 Asian Cup. The methodology used is descriptive qualitative research which describes everything that happens in the video matches. Sources of information come from four licensed trainers. Data collection methods involve (1) interviews, (2) observation, (3) documentation. The research findings include, (1) strength includes: having a good attacking strategy, transitioning from offensive to defend or from defending to offensive that is compact, due to having good physical health, and also being accompanied by a competent coach when reading the character of the opponent's game and making decisions while the game is running. (2) Weaknesses include: lack of flying hours in the international arena, and also lowering the rhythm of the defend tempo to half the court after scoring the first goal so that the opposing team can dominate the match. (3) opportunity includes: the Indonesian team has a fifty-fifty chance when competing in the quarter-finals, with the mental support of the players 20% -30% will be maintained, but loses the moment in the last second which ends up losing the match. (4) threats include: threats received by the Indonesian team in terms of tactics are still losing because they still cannot escape from the opponent.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".