ANALISIS STRATEGI SMK3 BERKELANJUTAN DENGAN PENDEKATAN SWOT PADA KONTRAKTOR LISTRIK DI PADANG
Bibliographic record
Abstract
The lack of workers' understanding of the importance of OHS (Occupational Health and Safety) contributes to the high rate of workplace accidents in electrical projects. This study aims to identify internal and external factors affecting the implementation of OHS Management Systems (SMK3) among electrical contractors and to formulate strategies using SWOT analysis. Data was collected through observations and questionnaires, then analyzed using IFAS, EFAS, IE Matrix, and SWOT.The IE Matrix results indicate that the company is in the "Growth and Build" category with an IFAS score of 3.8835 and an EFAS score of 4.0000, reflecting significant external pressures and opportunities for growth. Recommended strategies include improving SMK3 practices and intensifying SMK3 program development. SWOT strategies include: (1) S-O Strategy: enhancing SMK3 implementation and increasing workforce competency; (2) W-O Strategy: providing OHS training for workers; (3) S-T Strategy: conducting education and outreach on SMK3 implementation; and (4) W-T Strategy: developing detailed and strict OHS regulations. This approach aims to reduce workplace accidents.Keywords: Implementation Strategy, SMK3, IFAS, EFAS, SWOT
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".