Virtual Musik Gamelan Dengan Menggunakan Sensor Kinect
Bibliographic record
Abstract
Perkembangan seni musik saat ini menjadikan generasi muda dari budaya musik tradisional, salah satunya adalah musik gamelan. Generasi muda lebih muyukai hiburan berupa band, game yang didukung dengan teknologi yang canggih sedangkan gamelan sudah mulai ditinggalkan. Usaha untuk mendekatkan kembali generasi muda pada musik tradisional gamelan dengan cara membuat musik virtual. Perancangan virtual musik gamelan terdiri dari gerakan pada tangan kanan operator dengan menggunakan sensor kinect. Variasi nada pada Virtual musik gamelan terdiri dari 6 nada. Penelitian diharapkan dapat membantu meningkatkan minat generasi muda untuk memainkan musik gamelan. Metode pengujian pada penelitian ini termasuk pengumpulan data, analisa data, perancangan aplikasi dan teori interaksi desain. Pengujian virtual musik gamelan dengan oleh sepuluh orang pengguna diantaranya adalah anak-anak dan dewasa. Virtual musik gamelan mudah diimplementasikan karena tampilan yang user friendly dan gerakan yang dilakukan seakan akan secara alami.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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".