Penerapan Metode Clustering Pada Kasus Kecelakaan Kerja
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Work accidents are one of the problems that often occur in companies/agencies where accidents happen to employees/workers and cause serious physical injuries. BPJS Employment is an insurance program that is trusted by agencies/companies by claiming Work Accident Insurance (JKK) which can help in easing the financial burden on families as well as initial efforts to handle cases of work accidents that occur. The main aim of this research is to assist companies in handling work accident cases that occur. The data used in this research includes work accident reports collected from the Bpjs Ketenagakerjaan Stabat office. The method used is the clustering method with the K-means algorithm, which was chosen because of its ability to group fairly large amounts of data with fast and efficient computing time. By using the clustering method that has been used to process work accident case data at Bpjs Ketenagakerjaan in Stabat, we can produce new information from the 672 data that have been tested. From 672 work accident case data at Bpjs Employment in Stabat, 3 clusters were obtained with the results of Cluster 1 having 2 work accident case data, Cluster 2 having 9 work accident case data and Cluster 3 having 9 work accident case data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it