PENGARUH LINGKUNGAN KERJA FISIK DAN NON FISIK TERHADAP KINERJA PEGAWAI PADA LEMBAGA LAYANAN PENDIDIKAN TINGGI WILAYAH II
Bibliographic record
Abstract
Abstrak: Penelitian ini bertujuan untuk mengetahui pengaruh lingkungan kerja fisik dan lingkungan kerja non fisik terhadap kinerja karyawan. Populasi dalam penelitian ini adalah pegawai Lembaga Layanan Pendidikan Tinggi Wilayah II dengan teknik sampling jenuh sebanyak 50 orang. Teknik pengumpulan data menggunakan kuesioner dan metode analisis data yang digunakan dalam penelitian ini adalah metode analisis deskriptif dan kuantitatif menggunakan SPSS versi 25. Hasil penelitian ini adalah lingkungan kerja fisik berpengaruh positif dan signifikan terhadap kinerja karyawan. lingkungan kerja non fisik berpengaruh positif namun tidak signifikan terhadap kinerja karyawan. Kata Kunci: Lingkungan kerja fisik, Lingkungan Kerja Non-fisik, Kinerja Karyawan. Abstract: This study aims to determine the effect of the physical work environment and non-physical work environment on employee performance. The population in this study were 50 employees of the Regional II Education Service Institution with a saturated sampling technique. The data collection techniques used questionnaires and data analysis methods used in this study were descriptive and quantitative analysis methods using SPSS version 25. The results of this study were that the physical work environment had a positive and significant effect on employee performance. Non-physical work environment has a positive but not significant effect on employee performance. Keyword: Physical Work Environment, Non-Physical Work Environment, 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".