Pengaruh Lingkungan Kerja dan Motivasi terhadap Disiplin Serta Dampaknya pada Kinerja Pegawai Aparatur Sipil Negara Dinas Perhubungan Provinsi Jambi
Bibliographic record
Abstract
The purpose of this study is to describe the work environment, work motivation, work discipline and employee performance at the Jambi Province Transportation Service and to analyze the effect of the work environment and work motivation partially and simultaneously directly and indirectly on work discipline and employee performance, as well as to analyze work discipline on employee performance. To answer the research objectives, this research is supported by theoretical studies related to research variables, namely work environment, work motivation, work discipline, and performance. In addition, the authors also look for relevant previous research in the form of previous articles/journals and theses to support this research. The population in this study were employees of the Jambi Province Transportation Service, totaling 61 employees with conditions in 2022. The data analysis technique used in this study was through path analysis followed by hypothesis testing through the F test (Simultaneous) and t test (Partial). From the path analysis test carried out, it was found that the work environment and work motivation have an influence on work discipline and employee performance, both directly and indirectly. This explains that if the organization has a good work environment and the better the work environment then this will further improve employee discipline towards the organization and provide maximum work results.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".