QUALITY ANALYSIS OF THE FARINA BEAUTY CLINIC MOBILE APPLICATION USING THE SERVICE QUALITY (SERVQUAL) METHOD
Bibliographic record
Abstract
Farina Beauty Clinic is one of the beauty clinics which is a beauty clinic in Karawang that handles facial and body skin beauty problems. Farina Beauty Clinic really prioritizes customer satisfaction so that they always look beautiful, healthy and youthful in accordance with the expectations and desires of customers as well as current trends. which was adopted by Farina who already exists at Farina Beauty Clinic, namely Farina Beauty Clinic Mobile. This study aims to determine the level of quality and the factors that drive the Farina Beauty Clinic Mobile application using the Service Quality (Servqual) method. The sampling method used in this research is Non-Probability Sampling with purposive sampling technique, with the number of respondents as many as 158 respondents. The results of this study indicate that all indicators X1, X2, X3, X3, X4 and X5 on the t test and the successive test have a significant effect on the user's application quality because they have a sign value of 0.001 t Table. Farina Beauty Clinic is expected to be able to maintain indicators that have satisfied its performance and improve low performance attributes so that users are satisfied with the performance provided by Farina Beauty Clinic to users.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".