Analisis Kepuasan Konsumen Ditinjau dari Harga dan \nKualitas Pelayanan di Goro Assalaam Pabelan Sukoharjo
Bibliographic record
Abstract
The purpose of this study is to examine the variables that influence customer \nsatisfaction. This study uses a quantitative approach. The data used in this study is the \nprimary data. Primary data sources in this study were obtained directly from the \nrespondents. Sample in this study is all consumers who come and buy something in \nGoro Assalaam Pabelan Sukoharjo store. Sampling technique used in this study is \nconvennience sampling by taking samples of 130 respondents. Data collection \nmethods used were field studies with personal questionnaire distribution. The \nanalysis used in this research is test instrument of research, test of classical \nassumption, multiple linear regression analysis test, t test, F test and coefficient of \ndetermination (R2). The results obtained from this study have several findings. Price \nhas positive affect to customer satisfaction. Quality of service has positive affect to \ncustomer satisfaction. Simultaneously Price and Quality of service have significant \neffect on customer satisfaction with contribution given (Adjusted R2) equal to 30,2% \nand the rest 69,8% influenced by other variable not included in this research model.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".