Effect of Education and Training, Discipline Working and Job Satisfaction of Members of the Organization of Work Performance Bonding Kindergarten Teacher Indonesia (IGTKI) SUB Krembung
Bibliographic record
Abstract
This research attempts to know if there are no influence or between education and training discipline of work and the satisfaction work simultaneously and partial achievement work against member organization of IGTKI Krembung Distric. The population of this study is all member organization of IGTKI Krembung Distric and sample 98 employees. An instrument data collection is the questionnaire, and data analysis technique available for analysis is linear regression worship of idols with the program statistic spss. This research result indicates that the education and training, discipline of work and the satisfaction of work achievement of work affecting employees simultaneously and partial.proven the analysis that shows the education and training that the value of work is 2,444 t-test 0,016 with a sig value so (0,016 &< 0.05), discipline that the value of work is 2,947 t-test 0.004 with a sig value so (0.004 & < 0.05), and the satisfaction of work is the value of t-test 0,012 2,569 with a sig value so (0,012 & < 0.05) that means each variable x impact on the achievement of working. But the results obtained by the simultaneous or f-test 7,045 with the significance of 0,000 & < 0.05 so ho rejected ha accepted. So, can be concluded that the results of this research work is education and training, discipline of work and the satisfaction of work achievement of having influence in the work of employees. As we know that the education and training, discipline and satisfaction to improve the quality of work achievement and quality of the company.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".