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Record W4402695337 · doi:10.61132/jupiter.v2i5.561

Sistem Pendukung Keputusan Penentuan Kualitas Kayu untuk Kerajinan Mebel menggunakan Metode Electre Studi Kasus PT. Asia Mujur

2024· article· en· W4402695337 on OpenAlexaff
Andre Adrian, Rusmin Saragih, Magdalena Simanjuntak

Bibliographic record

VenueJupiter Publikasi Ilmu Keteknikan Industri Teknik Elektro dan Informatika · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsELECTREComputer scienceMathematicsOperations researchMultiple-criteria decision analysis

Abstract

fetched live from OpenAlex

Wood is the main element that determines the quality of a furniture product or other wooden crafts. Furniture was originally a wood carving craft industry, so that the furniture products produced emphasize the artistic aspect (carvings). The lack of knowledge of furniture companies and laypeople in this industry results in difficulties in determining the decision to choose wood to be used as a material for good and quality furniture crafts. Determining the quality of wood for furniture crafts needs to be strengthened by the increasing needs of the furniture industry. This industry has a high demand for quality wood raw materials to produce durable and aesthetic furniture products. Therefore, determining the quality of wood is crucial in ensuring the success of production and customer satisfaction..

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.003

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 head0.018
GPT teacher head0.236
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJupiter Publikasi Ilmu Keteknikan Industri Teknik Elektro dan InformatikaSame topicManagement and Optimization TechniquesFrench-language works237,207