PEMANFAATAN APLIKASI GOOGLE MEET PADA MATA KULIAH TEKNIK PROYEKSI BISNIS SEMESTER GASAL TAHUN PELAJARAN 2020/2021 DI UNIVERSITAS DIRGANTARA MARSEKAL SURYADARMA (UNSURYA) (Studi pada Mahasiswa Prodi Manajemen Kelas G)
Bibliographic record
Abstract
Abstrak: Penelitian ini bertujuan untuk penelitian ini mempunyai tujuan untuk 1) Mengetahui tanggapan tentang kemudahan mengakses Google Meet, 2) Mengetahui kemampuan mahasiswa dalam memahami materi yang disampaikan melalui aplikasi Google Meet, serta 3) Mengetahui efektifitas penggunaan aplikasi Google Meet mahasiswa Unsurya. Metode yang digunakan adalah penelitian kualitatif dengan pendekatan deskriptif, menggunakan data primer pada Tahun Ajaran 2020/2021 dengan populasi mahasiswa mata kuliah Teknik Proyeksi Bisnis semester Gasal Kelas G dengan sampel sebanyak 24 orang yang diambil secara purposive sampling. Hasil penelitian menunjukkan bahwa 92 persen mahasiswa menyatakan kemudahan dalam mengakses Google Meet selama pembelajaran daring, 79 pesen mahasiswa tetap bisa memahami pembelajaran daring melalui Google Meet. 95,83 persen mahasiswa menyetujui efektifitas penggunaan Google Meet dalam pembelajaran daring. Hal ini menunjukkan bahwa sebagian besar mahasiswa tidak terkendala penggunaan Google Meet dalam pembelajaran daring. Kata kunci: daring, PTJJ, Google Meet, Universitas Dirgantara Marsekal Suryadarma
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.021 |
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".