ANALISIS POTENSI RISIKO K3 DENGAN METODE HIRARC (Hazard Identification, Risk Assesment and Risk Control) DI LABORATORIUM MIKROBIOLOGIFAKULTAS KEDOKTERAN UNAND
Bibliographic record
Abstract
Pada data kasus kecelakaan kerja umumnya disebabkan oleh kurangnya penerapan budaya K3 (Keselamatan dan Kesehatan Kerja) di tempat kerja, tidak hanya menyebabkan kematian, kerugian materi moril, dan kerusakan lingkungan namun juga mempengaruhi produktivitas serta kesejahteraan masyarakat. Laboratorium merupakan tempat berkembangnya ilmu pengetahuan melalui berbagai macam penelitian dan percobaan. Penelitian bertujuan untuk mengenali jenis-jenis risiko dan tingkat bahaya K3 yang terjadi di Laboratorium Mikrobiologi Fakultas Kedokteran UNAND dan untuk memperkecilkan terjadinya potensi kecelakaan kerja, pada penelitian ini menggunakan metode HIRARC. Dari hasil penelitian didapat 40 potensi bahaya kecelakaan kerja di Laboratorium Mikrobiologi FK UNAND, terdapat 21 potensi bahaya kecelakaan kerja yang tingkat penilaian low (rendah), 11 potensi bahaya kecelakaan kerja tingkat medium (sedang) dan 8 resiko kecelakaan kerja dengan tingkat high (tinggi). Jika dipresentasekan terdapat 52% tingkat risiko low (rendah), 28% tingkat risiko medium (sedang) dan 20% tingkat risiko high (tinggi). Pengendalian risiko yang dapat dilakukan untuk mengurangi tingkat kecelakaan kerja seperti: membaca SOP sebelum bekerja, menyediakan dan memakai APD lengkap, penyediaan antiseptik dan P3K, penyediaan APAR, pemasangan rambu-rambu peringatan, pengecekan lampu yang kurang terang atau tidak menyala, service AC berkala dan pembatasan orang di dalam laboratorium, serta memberi sanksi kepada yang melangar aturan. Kata kunci: K3, Laboratorium Mikrobiologi, HIRARC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".