Pengaruh Total Quality Management terhadap Kualitas Layanan dan Kepuasan Mahasiswa Pada Fakulatas Bisnis dan Ekonomika Universitas Surabaya
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui bagaimana pengaruh Total Quality Management (TQM) terhadap kualitas layanan dan kepuasan mahasiswa dengan cara menyebarkan kuesioner kepada mahasiswa Fakultas Bisnis dan Ekonomika Universitas Surabaya. Metode pengambilan sampel yang digunakan adalah purposive sampling diperoleh sampel sebanyak 102 sebagai subjek penelitian. \nVariabel independen dalam penelitian ini adalah Total Quality Management(TQM) sedangkan variabel dependennya adalah kualitas layanan dan kepuasan konsumen. Metode analisis yang digunakan dalam penelitian ini adalah analisis regresi linier sederhana. Hasil penelitian menunjukkan bahwa Total Quality Management berpengaruh signifikan terhadap Kualitas Layanan pada FBE Universitas Surabaya, dan Total Quality Management berpengaruh signifikan terhadap Kepuasan Mahasiswa pada FBE Universitas Surabaya. \n
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 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".