Pengaruh Pelatihan , Kompensasi dan Kepemimpinan terhadap Prestasi Kerja Karyawan pada PT. Bredero Shaw Indonesia
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengidentifikasi pengaruh pelatihan, kompensasi dan kepemimpinan terhadap prestasi kerja karryawan di PT. Bredero Shaw Indonesia (BSI) yang berlokasi di Pulau Batam, sebuah perusahaan PMA dari Canada yang bergerak di bidang pipe coating. Dilaksanakan melalui pendekatan kuantitatif dengan mengunakan metode survey dengan kuesioner. Responden adalah 74 karyawan yang ditarik secara acak dari 90 karyawan yang bekerja di Unit 3L Plant PT. BSI Batam. Analisis regresi linier digunakan untuk mengidentifikasi pengaruh antar variabel menggunakan perangkat lunak Minitab 15. Hasil analisis data menunjukkan pelatihan, kompensasi dan kepemimpinan memberikan pengaruh yang nyata terhadap prestasi kerja karyawan PT. BSI Batam (Rsq = 64.3%). Secara parsial hasil analisis data menunjukkan bahwa pelatihan dan kepemimpinan memberikan pengaruh positif dan signifikan terhadap prestasi kerja karyawan (masing-masing Rsq = 52.1%, dan 56.5%). Sedangkan kompensasi tidak memberikan pengaruh yang nyata terhadap restasi kerja (Rsq = 6.4%). Hasil analisis data juga menunjukkan bahwa kepemimpinan memberikan pengaruh yang paling besar di antara ke 3 variabel independen yang digunakan. Walaupun kompensasi tidak berpengaruh terhadap prestasi kerja, tetapi disarankan kepada manager PT.BSI Batam untuk terus menerapkan gaya kepemimpinan yang mampu memotivasi karyawannya meningkatkan keterampilan untuk meningkatkan kinerja organisasi secara optimal.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".