Pengaruh Aktivator HCL dalam Arang Tempurung Kelapa Guna Menurunkan Kadar COD, BOD, dan TSS pada Limbah Cair Tahu
Bibliographic record
Abstract
Indonesia merupakan negara agraris yang sebagian besar penduduknya memiliki mata pencaharian sebagai petani, khususnya di wilayah Cilacap Jawa Tengah. Tanaman kelapa (Cocus nucifera. L) merupakan tanaman tropis yang tumbuh subur di Indonesia dan dikenal oleh masyarakat dan memiliki berbagai kegunaan. Namun, pemanfaatan tanaman kelapa umumnya hanya terbatas pada daging buahnya saja untuk diolah menjadi santan, sehingga bagian lain dari tanaman kelapa, seperti tempurung kelapa cenderung berpotensi sebagai limbah dan kurang dimanfaatkan secara optimal[1]. Tempurung kelapa dapat dijadikan arang aktif menggunakan aktivator HCL dengan metode yang sederhana dan ekonomis. Jumlah konsentrasi aktivator yang digunakan adalah 1N dan 3N. Dengan menggunakan metode adsorbsi dan filtrasi untuk mengolah limbah cair tahu didapatkan penurunan kadar COD, BOD, dan TSS yang berbeda. Pada penggunaan aktivator HCL 1N didapat hasil penurunan COD, BOD, dan TSS secara berurutan sebesar 20%, 23%, dan 73%. Hasil tersebut diperoleh setelah sampel mengalami adsorbsi dan filtrasi selama 2 jam. Sedangkan efektivitas penggunaan aktivator HCL 3N menghasilkan penurunan kadar COD, BOD, dan TSS sebesar 28%, 31%, dan 76%.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".