Pengaruh Penerapan Keselamatan Dan Kesehatan Kerja (K3) Serta Lingkungan Kerja Terhadap Produktivitas Kerja Di Surabaya
Bibliographic record
Abstract
ABSTRAK: Semua pekerjaan dituntut agar dapat menghasilkan kualitas yang baik dengan waktu yang telah ditentukan. Salah satunya adalah pekerjaan proyek konstruksi yang saat ini mengalami perkembangan pesat di Indonesia dengan memperhatikan produktivitas kerja. Proyek konstruksi tidak lepas dari keselamatan dan kesehatan kerja yang bisa menunjang suatu pekerja bisa menyelesaikan pekerjaan. Serta lingkungan kerja yang mendukung suatu pekerjaan. Keselamatan dan kesehatan kerja serta lingkungan kerja bisa mempengaruhi produktivitas kerja di suatu proyek konstruksi. Teknik pengambilan penelitian menggunakan metode non probability sampling disertai teknik purposive sampling dengan cara penyebara kuesioner. Responden penelitian adalah mandor, tukang dan pekerja kasar. Penyebaran kuesioner di 3 proyek, yaitu proyek pembangunan Rumah Sakit Katolik St Vicentius Paulo (RKZ), proyek pembangunan Apartmen Puncak Merr, dan proyek pembangunan Apartmen Belleview Manyar. Berdasarkan hasil analisis penelitian menyatakan bahwa variabel keselamatan dan kesehatan kerja (X1) berpengaruh signifikan terhadap produktivitas kerja (Y) dengan nilai (T-statistic=14,487 dangt; 1,96) dan (P-values = 0,000 danlt; 0,05). Sedangkan untuk variabel lingkungan kerja (X2) berpengaruh signifikan terhadap produktivitas kerja (Y) dengan nilai (T-statistic= 3,962 dangt; 1,96) dan (P-values = 0,000 danlt; 0,05).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".