Analisis Risiko Penyakit dan Kecelakaan Kerja Menggunakan Model Upaya Kesehatan Kerja pada Home Industry Batik Pekalongan
Bibliographic record
Abstract
Batik is one of the cultural heritages which is experiencing very rapid development, especially in small, medium and even large scale industries. The home batik industry in Pekalongan city can directly cause safety and health problems for its workers caused by the main factors in the production and work environment. Therefore, it is necessary to carry out further analysis of the risk factors due to work and work accidents that can occur in batik workers. This type of research is a descriptive observational study with an analytical approach. The population in this study is UMKM located in the Pekalongan City Region. The results obtained are 70% of batik workers are women, 90% of respondents are > 48 years old, as many as 78 & respondents have a working period of > 10 years. Conditions in the environment and activities in the batik industry can pose a risk to work accidents, this can be caused by a dusty work environment, using hot wax, the process of applying written and stamped batik, using chemicals in the coloring process, removing starch (pelorodan process). use hot water. So it can be concluded that the risk of occupational disease in craftsmen due to work on batik includes respiratory problems, skin irritation, fatigue, while the highest risk of work accidents is burns. Therefore, it is recommended that workers be more aware of the risk of occupational diseases and work accidents as a result of one of the consequences of work and the work environment so that occupational health and safety for individual work can be achieved.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".