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Record W4406081054 · doi:10.54259/satesi.v4i1.3203

Analisa Penggunaan Hasil Pembangunan KRI Alugoro-405 di Galangan PT. PAL Surabaya untuk Mendukung Operasional Kapal Selam TNI AL

2024· article· en· W4406081054 on OpenAlexaff
Alfredo Panataran Purba, Firman Johan, Ugik Cahyo

Bibliographic record

VenueSATESI Jurnal Sains Teknologi dan Sistem Informasi · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

The current condition of KRI Alugoro-405 after completion of its construction in 2019 by PT PAL and DSME is that there are corrosion problems on the ship's body, HVAC air humidity system and electrical problems. it is necessary to analyze the root causes of these problems. As an analytical approach, the researcher adopts a descriptive qualitative method to produce a more in-depth and flexible research to find out the root causes of technical problems that occur by processing data using the Nvivo application. Also carried out a strategy analysis to measure the strengths and weaknesses of using the results of the construction of the Kri Alugoro-405 submarine using SWOT analysis. the types of data used in this study include primary data through interviews and observations and secondary data obtained from documents. The results of this study include 1) Corrosion on KRI Alugoro-405 is caused by coating applications and construction designs that are not in accordance with standards and lack of protection by cathodic protection systems. 2) Problems with the HVAC system are caused by sub-optimal design and insufficient diameter of the battery room ventilation branches. 3) In the electrical system, there were voltage spikes in the AC network and voltage distortions due to connected loads, such as inrush current and harmonics, as well as insulation problems in the DC-AC converter.

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 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), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.843
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.241
Teacher spread0.225 · 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 teacher head, not a consensus.

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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