ANALISIS TINGKAT KEPUASAN MASYARAKAT TENTANG LAYANAN SERTIFIKASI HALAL DI KABUPATEN SUMBAWA
Bibliographic record
Abstract
This study aims to determine the level of community satisfaction regarding halal certification services in Sumbawa Regency. This research uses a quantitative descriptive approach. The type of data used in this study is quantitative data obtained directly from primary sources using a questionnaire as an instrument for data collection. To obtain an overview of the level of public satisfaction with the performance of halal certification services organized by the Halal Center of Sumbawa Regency, a simple approach was used referring to the Minister of Administrative and Bureaucratic Regulation Number 14 of 2017 concerning measuring the index of public satisfaction with public services. Based on the results of the research that has been carried out, it is concluded that the implementation of halal certification services at the Halal Center of Sumbawa Regency generally reflects a good level of quality. The average value of SMEs after conversion is 86.53 and is in the Good Category. This means that the Halal Center of Sumbawa Regency has served halal certificate applicants well, although there are several aspects that still require improvement. The service element that received the highest assessment from respondents was the behavior of service implementers, while the service element that received the lowest assessment from respondents was the availability of service facilities and infrastructure.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".