Analisa Penggunaan Hasil Pembangunan KRI Alugoro-405 di Galangan PT. PAL Surabaya untuk Mendukung Operasional Kapal Selam TNI AL
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".