Evaluasi Kualitas Air pada Badan Sungai Kali Biru Akibat Limbah Cair Industri Tahu di Desa Taman Agung, Kecamatan Muntilan, Kabupaten Magelang, Jawa Tengah
Bibliographic record
Abstract
Industri tahu yang terdapat di Desa Taman Agung, Kecamatan Muntilan, Kabupaten Magelang, Jawa Tengahmerupakan industri tahu rumahan skala kecil yang telah beroperasi sejak tahun 1992 dan membuang langsunglimbah cair hasil produksi ke Sungai Kali Biru. Dampak yang dirasakan masyarakat berupa timbulnya bau,perubahan warna, dan munculnya endapan pada sungai. Sungai Kali Biru dimanfaatkan oleh masyarakat sebagaisumber air pada lahan pertanian. Tujuan penelitian ini antara lain mengetahui kualitas limbah cair industri tahu,mengetahui status mutu air Sungai Kali Biru akibat limbah cair industri tahu dengan metode indeks pencemaran(IP), dan melakukan evaluasi kualitas air buangan dengan metode stream standard. Metode yang digunakandalam pengambilan sampel limbah cair tahu dan air sungai adalah metode purposive sampling. Hasil penelitianmenunjukkan parameter BOD, COD, dan pH tidak sesuai dengan baku mutu. Ketidaksesuaian nilai tersebut sertaterjadinya peningkatan nilai parameter TSS setelah outlet air limbah tahu diasumsikan bahwa limbah cair tahusebagai pencemar air Sungai Kali Biru. Nilai indeks pencemaran pada titik sampel 7 sebesar 0,4954 memenuhibaku mutu (kondisi baik) dan titik 15 sebesar 4,8533 tercemar ringan. Nilai evaluasi kualitas air sungaiparameter BOD sebesar 2,1208, COD sebesar 16,0786, TSS 0,05 dan pH sebesar 7,0996.Kata kunci: Industri tahu; limbah cair tahu; status mutu air
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".