Pengaruh Motivasi, Stress Kerja dan Lingkungan Kerja Fisik Terhadap Kinerja Karyawan pada Balai Penyelidikan dan Pengembangan Teknologi Kebencanaan Geologi (BPPTKG) Daerah Istimewa Yogyakarta
Bibliographic record
Abstract
This study aims to determine and evaluate the effect of motivation, work stress, and physical work environment on employee performance at the Center for Research and Development of Geological Disaster Technology (BPPTKG) Special Region of Yogyakarta. The sample in this study were employees of the Special Region of Yogyakarta Special Region of Yogyakarta with 70 respondents. The sampling technique applied is non-probability sampling where all members of the population are sampled. Data collection was carried out through questionnaires. The data analysis method used in this study is multiple linear regression analysis which aims to calculate the magnitude of the regression coefficient to show the magnitude of the influence of motivation, work stress and physical work environment on employee performance. While the observation test shows that various changes in motivation, work stress and physical work environment jointly affect employee performance in the special area of the Geological Disaster Technology Investigation and Development Agency (BPPTKG) Special Region of Yogyakarta. The results of this research show that: (1) motivation has no significant effect on employee performance; (2) work stress has no significant effect on employee performance; (3) the physical work environment has a significant effect on employee performance.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".