ANALISIS TUNJANGAN REMUNERASI DAN PENEMPATAN KERJA TERHADAP KINERJA DENGAN VARIABEL MODERASI MOTIVASI KERJA PADA BPJS KETENAGAKERJAAN WILAYAH SULAWESI DAN MALUKU
Bibliographic record
Abstract
Penelitian ini bertujuan untuk menganalisis dan menguji pengaruh tunjangan remunerasi dan penempatan kerja terhadap kinerja pegawai, serta bagaimana motivasi kerja berperan sebagai variabel moderasi pada BPJS Ketenagakerjaan Wilayah Sulawesi dan Maluku. Metode yang digunakan adalah penelitian kuantitatif dengan metode survei. Data dikumpulkan melalui observasi, dokumentasi, dan kuesioner dari 80 responden. Hasil penelitian menunjukkan bahwa tunjangan remunerasi dan penempatan kerja memiliki pengaruh positif dan signifikan terhadap kinerja pegawai. Selain itu, motivasi kerja dapat memperkuat pengaruh tunjangan remunerasi dan penempatan kerja terhadap kinerja pegawai. Dengan demikian, penelitian ini memberikan wawasan penting bagi organisasi dalam meningkatkan kinerja pegawai melalui pengelolaan tunjangan dan penempatan kerja yang tepat. This study aims to analyze and test the effect of remuneration allowances and job placement on employee performance, as well as how work motivation acts as a moderating variable at the BPJS Ketenagakerjaan Offices in the Sulawesi and Maluku Regions. The method used is quantitative research with a survey approach. Data collected through observation, documentation, and questionnaires from 80 respondents. The results reveal that remuneration allowances and job placement have a positive and significant impact on employee performance. Furthermore, work motivation can strengthen the influence of both remuneration allowances and job placement on employee performance. Thus, this research provides valuable insights for organizations in improving employee performance through effective management of allowances and job placements.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".