Korelasi Antara Karakteristik TKI dengan Jenis Pekerjaan Menggunakan Metode Apriori
Bibliographic record
Abstract
This study addresses the issue of Indonesian migrant workers (TKI) whose characteristics do not match the jobs assigned abroad, often leading to complaints from agencies and companies. This mismatch is caused by incorrect job placements and insufficient training, which prompts TKI to leave their assigned jobs. The research aims to better understand the characteristics of TKI that influence successful job placement. The **apriori** method was used to identify patterns and relationships between TKI characteristics, destination countries, and suitable job types. Based on a 30% minimum support, 3 and 4 itemset combinations were produced, showing correlations between TKI characteristics and job positions. Using lowerboundminsupport 0.001 and minmetric 0.1, this study generated 6 itemsets from 13 data points, providing significant correlations between TKI characteristics and more accurate job placements.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".