Analisis Pengaruh Kualitas Pelayanan dan Kualitas Produk Terhadap Kepuasan Pelanggan di Resto Kita Karawang
Bibliographic record
Abstract
The aim of the research was to obtain an in-depth understanding of the influence of service quality and product quality on the level of customer satisfaction at Resto Kita Karawang. This research uses quantitative methods in its research methodology. Using a questionnaire to collect and analyze data from customers who meet the criteria of having made at least two purchases from Resto Kita Karawang. And involving 96 respondents as research samples selected through a purposive sampling method. The analysis was carried out using multiple linear regression analysis. The results of the research show that customer satisfaction is influenced by service quality and product quality at Resto Kita Karawang. These results can be understood from the calculations that have been carried out which show that the variables of service quality and product quality have great value in customer satisfaction. Resto Kita Karawang needs to focus on improving its quality and maintaining this quality to strengthen customer satisfaction. There are implications that by always being consistent in maintaining customer satisfaction and always maintaining quality, it can create a high sense of loyalty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".