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Record W2935704984

PENERAPAN FUZZY MAMDANI PADA PENILAIAN KINERJA DOSEN (STUDI KASUS STMIK KAPUTAMA BINJAI)

2017· article· id· W2935704984 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageid
FieldBusiness, Management and Accounting
TopicDecision Support System Applications
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsFuzzy logicVariable (mathematics)Value (mathematics)Computer scienceMathematicsArtificial intelligenceStatistics
DOInot available

Abstract

fetched live from OpenAlex

This research is aimed to apply Mamdani Method to get function optimization value quickly in lecturer performance assessment. Input consists of 3 (three) variables are: Variable Material, Variable Discipline and Attitude Variables. The results obtained in this study the value of function that has been optimized which will get the best lecturer in performance. Keywords: Mamdani, Optimization

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.

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), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.588
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0070.003
Open science0.0030.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0370.128

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 head0.039
GPT teacher head0.284
Teacher spread0.245 · 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

Quick stats

Citations12
Published2017
Admission routes1
Has abstractyes

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