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Perencanaan Strategis Sistem Informasi untuk Mendukung Mutu dan Produktif ( Studi Kasus: PT. Mitrametal Perkasa )

2017· article· id· W2779450563 on OpenAlexaff
Baenil Huda

Bibliographic record

VenueTechno Xplore Jurnal Ilmu Komputer dan Teknologi Informasi · 2017
Typearticle
Languageid
FieldComputer Science
TopicBlockchain Technology in Education and Learning
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Abstrak Saat ini kemajuan Teknologi Informasi bukan sesuatu hal yang asing bagi segala aspek kehidupan manusia. Terutama dalam bidang perusahaan yang pastinya mengharuskan penggunaan sebuah teknologi demi kelangsungan bisnis perusahaannya. Untuk itu suatu perusahaan mau tidak mau harus bersaing dalam memperbaiki proses bisnisnya, baik dalam segi sumber daya maupun strategis bisnis. Perusahaan harus melibatkan teknologi ini untuk mengolah seluruh sumber daya yang ada diperusahaan guna mendapatkan kinerja produktivitas baik dari segi pelayanan maupun hasil produksi. Adapun metodologi yang digunakan dalam penelitian ini adalah metode Ward And Peppard, yang mempunyai konsep terdiri dari tahapan masukan dan tahapan keluaran serta hasil akhir yaitu sebuah portofolio aplikasi pada masa yang akan datang Kata Kunci : Teknologi Informasi, Metodologi, Ward And Peppard,

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.048
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0070.005
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0480.015

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 head0.036
GPT teacher head0.283
Teacher spread0.247 · 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 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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