ANALISIS USER EXPERIENCE PADA GAME FIFA MOBILE DENGAN MENGGUNAKAN METODE GAME DESIGN FACTORS QUESTIONNAIRE
Bibliographic record
Abstract
FIFA Mobile merupakan permainan yang bertemakan simulasi sepak bola yang \ndikembangkan oleh EA Sports dan EA Canada diterbitkan oleh EA Sports untuk \nperangkat IOS dan Android pada Oktober 2016. Penelitian ini menganalisis kualitas \nUser Experience pada gim FIFA Mobile berdasarkan pengalaman pengguna \nmenggunakan metode Game Design Factors Questionnaire. Penelitian ini \ndilakukan secara kuantitatif. Teknik penentuan sampel dan populasi menggunakan \nSimple Random Sampling dengan total responden yang didapat sebanyak 105 responden dengan menggunakan rumus Lemeshow karena total populasi tidak \ndiketahui. Hasil analisis menunjukkan kategori cukup baik mendominasi pada \npermainan FIFA Mobile dengan total 7 faktor seperti game goals, game mechanism, interaction, freedom, game fantasy, sensation, dan mystery untuk dikembangkan lebih baik lagi
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".