Implementation Of Data Mining Grouping Of Old Age Guarantee (Jht) Based On Region In Pandemic Period
Bibliographic record
Abstract
During the COVID-19 pandemic, many companies experienced a decline or went bankrupt, so they had to reduce the number of workers and even close the company. BPJS Ketenagakerjaan is a public legal entity that is responsible to the president and functions to administer four programs, namely Work Accident Insurance (JKK), Death Insurance (JKM), Old Age Security (JHT), with the addition of the Pension Guarantee program ( JP). One of them is the submission of claims from too many participants of the Old Age Security program from various regions, especially the Langkat sub-district, so that it becomes a big problem to provide good service or information for the participants. For this reason, the author tries to create a system to support a computerized grouping process that can help automatically classify JHT claims by region, so there is an opportunity to design a grouping data mining system in it. Data mining is a process of mining data in very large amounts of data using statistical, mathematical methods, to utilize the latest artificial intelligence technology. Clustering is a method that is applied in creating a grouping data mining system to make it easier for employees to group JHT by region. Based on the analysis that has been done in the grouping of old-age insurance data using the clustering method, it is necessary to do the cluster process several times to get the same results according to the process that was first carried out, namely in cluster 1 : 2 3 2 cluster 2 : 2 8 2, cluster 3: 2 13 2 with 545 data in cluster 1, 308 data in cluster 2 and 421 data in cluster 3.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".