ANALISIS KOROSI BAJA ASTM A 36 PENGARUH ASAM SULFAT DENGAN VARIASI WAKTU PERENDAMAN DI LINGKUNGAN LAUT
Bibliographic record
Abstract
Pencemaran air di wilayah perairan pesisir Pantai Selatan Bantul Yogyakarta tidak hanya berdampak kepada makhluk hidup tetapi juga menimbulkan korosi pada kontruksi baja sehingga umur pakai material baja lebih singkat dan nilai ekonomis menurun. Mayoritas logam pada industri maritim adalah Baja ASTM A36 dengan kandungan karbon 0,25% sampai 0,29%. Tujuan penelitian ini untuk mengetahui variasi waktu perendaman terhadap laju korosi Baja ASTM A36. Medium perendaman menggunakan dua variasi yaitu medium NaCl 3,5% (medium air laut buatan) dan medium NaCl 3,5% + H2SO4 0,5 M. Variasi waktu perendaman digunakan 24, 48, dan 72 jam. Secara eksperimental, hasil uji immersion corrosion test menunjukkan nilai laju korosi tertinggi Baja ASTM A36 pada rendaman medium NaCl 3,5% + H2SO4 0,5 M dengan nilai 37,584 mmpy (24 jam), 31,965 mmpy (48 jam), dan 23,795 mmpy (72jam), sampel uji mengalami korosi seragam dan korosi batas butir. Nilai laju korosi tertinggi pada medium perendaman NaCl 3,5% terjadi pada Baja ASTM A36 dengan nilai 0,098 mmpy (24 jam), 0,105 mmpy (72 jam), 0,081 mmpy (120 jam), 0,063 mmpy (168 jam), sampel uji mengalami korosi seragam dan korosi sumuran. Hasil penelitian didapatkan adanya senyawa H2SO4 dapat mempercepat laju korosi di lingkungan laut.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".