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Record W4384573866 · doi:10.59697/jsik.v6i2.190

Implementasi Analisa Persediaan Barang Menggunakan Metode Apriori Pada Percetakan Indah Jaya Berbasis WEB

2022· article· id· W4384573866 on OpenAlexaff
Aulia Ananda, Siswan Syahputra, Lina Arliana

Bibliographic record

VenueJurnal Sistem Informasi Kaputama (JSIK) · 2022
Typearticle
Languageid
FieldComputer Science
TopicMultimedia Learning Systems
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsBusinessHumanitiesArt

Abstract

fetched live from OpenAlex

Percetakan adalah sebuah proses industri untuk memproduksi secara massal tulisan dan gambar, terutama dengan tinta di atas kertas menggunakan sebuah mesin cetak. Kegiatan percetakan ini bukan hanya masalah persediaan barang saja, namun menuntut sebuah sistem penempatan kerja yang akan membuat hasil transaksi dan persediaan menjadi lebih tersistematis dan komputerisasi. Dengan menggunakan metode apriori pada percetakan indah jaya berbasis web diharapakan untuk mengetahui persediaan barang di gudang dan mengetahui barang keluar dan masuk pada percetakanindah jaya.dalam perhitungan di dapatkan transaksi yang sering terjadi yaitu amplop dan kertas hvs. Maka dalam transaksi yang akan datanng akan dapat di analisi lebih baik.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0060.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0210.005

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 head0.020
GPT teacher head0.256
Teacher spread0.236 · 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 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
Published2022
Admission routes1
Has abstractyes

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