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Record W2937852761 · doi:10.31937/si.v9i2.991

Visualisasi Data Penjualan dan Produksi PT Nitto Alam Indonesia Periode 2014-2018

2019· article· en· W2937852761 on OpenAlexaboutno aff
Dessy Aryanti, Johan Setiawan

Bibliographic record

VenueUltima InfoSys Jurnal Ilmu Sistem Informasi · 2019
Typearticle
Languageen
FieldComputer Science
TopicData Mining and Machine Learning Applications
Canadian institutionsnot available
Fundersnot available
KeywordsDashboardProduction (economics)VisualizationQuarter (Canadian coin)BusinessData visualizationComputer scienceDatabaseMarketingGeographyEconomicsData mining

Abstract

fetched live from OpenAlex

PT Nitto Alam Indonesia is a Manufacturing company engaged in screw manufacturing services. The company has a total of 134,252 rows sales and production data, but the data has never been analyzed so that the information is still not fully explored. This research proposes to make a visualization in the form of a dashboard containing sales and production data at PT Nitto Alam Indonesia in 2014 – 2018. It will be shown by using visual data mining (VDM) method with Tableau Software tools. The purpose of this study was to assist PT Nitto Alam Indonesia in analyzing sales and production data to find information that had never been explored before. The results of this study are that the patterns of sales and production can be known for the last 5 years. The sales pattern is included in the type of cycle with the highest sales peak located in the fourth quarter in 2017 at 6.552%. In addition, the performance of sales and production from 2014 to 2018 increased consistently. This research has been validated by applying User Acceptance Tests at PT Nitto Alam Indonesia.

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)0.004
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 score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.007
Science and technology studies0.0000.000
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.004

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 head0.023
GPT teacher head0.286
Teacher spread0.263 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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