Analisis Kualitas Air Permukaan Akibat Limbah Peternakan Menggunakan Metode CCME WQI di Kalurahan Wijimulyo, Kapanewon Nanggulan, DIY
Bibliographic record
Abstract
Peternakan memiliki dampak positif bagi masyarakat dalam menunjang perekonomian, tetapi peternakan bisa memiliki dampak negatif jika limbah dari peternakan tidak diolah melainkan langsung dibuang ke lingkungan terutama badan air. Penelitian ini bertujuan untuk menganalisis kualitas air permukaan akibat limbah peternakan di Kalurahan Wijimulyo, Kapanewon Nanggulan, Kabupaten Kulon Progo, DIY. Metode yang digunakan pada penelitian yaitu Purposive Sampling dengan teknik Grab Sampling dengan pengambilan sesaat pada 2 titik dan pengambilan sampel sebanyak 4 kali. Perhitungan kualitas air permukaan menggunakan metode Canadian Council of Miniters of The Environment Water Quality Index (CCME). Hasil pengambilan sampel air permukaan didapatkan parameter BOD, COD, dan TSS melebihi baku mutu dengan nilai tertinggi BOD sebesar 209,5 mg/L di titik 1 pada pengambilan ke-4. Nilai COD tertinggi sebesar 304,5 mg/L di titik 1 pada pengambilan ke-3, serta parameter TSS tertinggi dengan nilai 569 mg/L di titik 1 pada pengambilan ke-1. Pada parameter pH dan Amoniak (sebagai Nitrogen) tidak melebihi baku mutu. Nilai kualitas pencemaran pada titik 1 sebesar 33,24 dengan klasifikasi Buruk, dan titik 2 sebesar 50,16 dengan klasifikasi Kurang. Hasil penelitian ini diharapkan dapat menjadi sumber informasi penelitian lebih lanjut serta menjadi acuan dalam pengolahan limbah peternakan.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.008 | 0.002 |
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".