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ANALISIS EFISIENSI BIAYA PRODUKSI MIE PADA UD MIE UJANG KABUPATEN JEMBER

2023· article· en· W4387456124 on OpenAlexaboutno aff
Nadia Isnaini, Sri Kantun, Dwi Herlindawati

Bibliographic record

VenueJurnal Akun Nabelo Jurnal Akuntansi Netral Akuntabel Objektif · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsRaw materialProduction (economics)Quarter (Canadian coin)Production costAgricultural scienceProduct (mathematics)Operations managementBusinessMathematicsEngineeringEnvironmental scienceEconomics

Abstract

fetched live from OpenAlex

This study aims to measure the level of efficiency of raw noodle production costs at UD Mie Ujang, Jember Regency with standard costs as a reference in the use of production costs. This research belongs to the type of quantitative descriptive research. The type of data used is the main data in the form of production cost report documents of UD Mie Ujang, Jember Regency for the first quarter of 2022 and supporting data in the form of interview results related to the condition of production cost reports. The research informants are the owners and employees of UD Mie Ujang, Jember Regency. The results showed that the use of raw noodle production costs at UD Mie Ujang, Jember Regency in the first quarter of 2022 showed efficient results. The use of production costs is more efficient in producing super raw noodles compared to ordinary raw noodles. The results of cost efficiency can be used as a consideration and decision in determining the selling price of the next product.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0010.000
Scholarly communication0.0020.004
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.022
GPT teacher head0.237
Teacher spread0.215 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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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