Pengaruh Keselamatan Dan Kesehatan Kerja Terhadap Produktivitas Kerja Karyawan Pada PT. XYZ Divisi 1 Bagian Pemanen
Bibliographic record
Abstract
The purpose of this study is to determine the effect of occupational safety and health on the productivity of PT. XYZ Division 1 Harvest Division. To achieve these objectives a study was conducted using a sample of 42 people, using the census method. The data analysis method uses simple linear regression. Based on test results on occupational safety and health variables (X) obtained tcount (3.751) is greater than the value of table (2.021) with a significant level of 0.01 < 0.05. So it can be concluded that Ho was rejected and Ha was accepted, which means that occupational health and safety had a significant effect on the productivity of the employees of PT. XYZ Division 1 Harvest Division. Based on the results of calculations to see how much the percentage of the contribution of the independent variable to the dependent variable, after processing the data it turns out that the value obtained (R2) = (0.510 X 0.510 = 0.260) or 26%, then the percentage of contribution or contribution of the independent variable to the dependent variable is 0.260 or 26 % while the remaining 74% is influenced by other variables that are ignored or not examined in this study. Recommendations that can be submitted are expected that occupational health and safety of employees must be tightened even more, such as prohibiting the deviation of dangerous goods because they received the lowest response of 3.31 on a statement of agreement, it can be said that occupational health and safety the main priority for employees is with employees
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".