Peningkatan Kinerja Pegawai Melalui Kemampuan Kerja, Motivasi Kerja dan Fasilitas Kerja di Kecamatan Eromoko Kabupaten Wonogiri
Bibliographic record
Abstract
Tujuan penelitian ini adalah untuk menganalisis dan mengetahui signifikasinya pengaruh kemampuan kerja, motivasi kerja dan fasilitas kerja terhadap kinerja pegawai Kecamatan Eromoko Kabupaten Wonogiri. Populasi dalam penelitian ini adalah pegawai Kecamatan Eromoko Kabupaten Wonogiri yang berjumlah 39 pegawai. Sampel diambil pada pegawai Kecamatan Eromko Kabupaten Wonogiri sebanyak 39 pegawai dengan metode sensus sampling. Analisis dalam penelitian ini terdiri dari, pengujian instrument : uji validitas dan uji reliabilitas, uji asumsi klasik, analisis regresi linear berganda, uji t, uji F dan uji R2. Hasil penelitian menunjukan bahwa : (1) kemampuan berpengaruh positif dan tidak signifikan terhadap kinerja pegawai; (2) motivasi berpengaruh positif dan signifikan terhadap kinerja pegawai; (3) fasilitas berpengaruh positif dan signifikan terhadap kinerja pegawai; (4) kemampuan, motivasi dan fasilitas berpengaruh signifikan terhadap kinerja pegawai; (5) koefisien determinasi (R2) didapatkan hasil sebesar 0,756 yang berarti variabel kemampuan kerja, motivasi kerja dan fasilitas kerja dapat menjelaskan 75,6% variabel kinerja pegawai, sedangkan sisanya sebesar 24,4% dijelaskan oleh variabel lain yang tidak teliti seperti disiplin kerja, komitmen organisasi, kompensasi, budaya organisasi dan lingkungan kerja.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 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".