MétaCan
Menu
Back to cohort
Record W2903654435 · doi:10.35314/isi.v3i1.258

Virtual Musik Gamelan Dengan Menggunakan Sensor Kinect

2018· article· id· W2903654435 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueINOVTEK Polbeng - Seri Informatika · 2018
Typearticle
Languageid
FieldComputer Science
TopicBlockchain Technology in Education and Learning
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsArtHumanities

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.007

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.

Opus teacher head0.014
GPT teacher head0.257
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it