Analisis Human Factors Pada Mekanik Aircraft Maintenance
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui gambaran human factors pada \nmekanik aircraft maintenance. Transport Canada mengidentifikasi dua belas \nhuman factors yang menurunkan kemampuan orang untuk menampilkan kinerja \nyang efektif dan aman, yang dapat menyebabkan kesalahan perawatan pesawat. \nKedua belas faktor ini yang dikembangkan oleh Gordon Dupont dikenal dengan \n“dirty dozen,” pada akhirnya diadopsi oleh industri penerbangan untuk membahas \nhuman error dalam perawatan pesawat (FAA, 2018). \nPenelitian ini menggunakan metode penelitian kualitatif dengan pendekatan \nstudi kasus instrumental. Penggalian data dilakukan dengan wawancara serta \npengumpulan dokumen. Teknik analisis data yang digunakan adalah analisis \ntematik pada hasil wawancara seluruh subjek. Penelitian ini melibatkan empat \norang subjek mekanik GMF AeroAsia yang pernah melakukan kesalahan kerja. \nBerdasarkan penelitian ini ditemukan bahwa keempat subjek memiliki \npengalaman kesalahan kerja yang berbeda – beda, namun terdapat kesamaan pola \npada proses kesalahan kerja itu terjadi. Keempat subjek mengalami complacency, \ndi mana subjek ceroboh dalam mengambil keputusan sehingga terjadi kesalahan \nkerja.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".