PENGARUH MOLARITAS ALKALI TERHADAP KUAT TARIK BELAH BETON GEOPOLIMER BERBASIS FLY ASH EX PLTU CIREBON POWER
Bibliographic record
Abstract
Penggunaan batu bara di Indonesia mencapai 15,6 juta metrik ton pada Februari 2018. Hasil dari pembakaran yang dihasilkan oleh batubara adalah bottom ash dan fly ash yang dikatagorikan limbah berbahaya dan beracun (B3). Persentase fly ash dari limbah pembakaran batu bara sekitar 80 – 90%, maka diperlukan pemanfaatan untuk mengurangi limbah fly ash tersebut. Beton geopolimer adalah beton yang menggunakan fly ash sebagai pengikat dengan penambahan alkali seperti natrium hidroksida dan natrium silikat. Konsetrasi molaritas natrium hidroksida yang dipakai yaitu 6M, 8M, 10M, 12M, 14M. Perbandingan alkali natrium hidroksida dengan natrium silikat adalah 2:1. pengujian kuat tarik belah dilakukan pada umur beton 28 hari. Dari hasil yang didapat bahwa molaritas natrium hidroksida lebih dari 10M mengurangi kekuatan beton geopolimer yang menggunakan fly ash dari PLTU Cirebon Power. Nilai kuat tarik belah beton geopolimer pada molaritas 6M, 8M, 10M lebih besar dibandingkan beton normal.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.020 | 0.004 |
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.
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".