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Record W4391117444 · doi:10.58406/jeb.v11i3.1369

ANALISIS TINGKAT KEPUASAN MASYARAKAT TENTANG LAYANAN SERTIFIKASI HALAL DI KABUPATEN SUMBAWA

2023· article· en· W4391117444 on OpenAlexaboutno aff
Roos Nana Sucihati, Dedy Heriwibowo, Andi Kusmayadi

Bibliographic record

VenueJurnal Ekonomi & Bisnis · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHalal products and consumer behavior
Canadian institutionsnot available
Fundersnot available
KeywordsCertificationBusinessService (business)CertificateQuality (philosophy)MarketingQuarter (Canadian coin)Data collectionOperations managementManagementEngineeringGeography

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.055
GPT teacher head0.337
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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