Penentuan Status Mutu Air Sungai Pekalongan Menggunakan Metode Indeks Pencemaran (IP) dan CCME
Bibliographic record
Abstract
Sungai Pekalongan merupakan salah satu sungai di Kota Pekalongan, Jawa Tengah. Sebagian besar masyarakat di Pekalongan bermata pencaharian sebagai pengusaha batik, baik yang home industry atau perusahan besar. Hal tersebut dapat menyebabkan penurunan kualitas air dan dapat menimbulkan pencemaran air karena limbah tersebut dibuang secara langsung ke Sungai Pekalongan. Penelitian ini bertujuan untuk mengetahui peubah yang menyebabkan pencemaran di Sungai Pekalongan serta menentukan dan membandingkan status mutu air Sungai Pekalongan menggunakan metode Indeks Pencemaran (IP) dan metode Canadian Council of Minister of the Environment (CCME). Penelitian ini dilaksanakan pada bulan Februari 2022. Metode penelitian yang digunakan yaitu metode survei. Penentuan titik lokasi sampling menggunakan metode Purposive sampling. Pengambilan sampel dilakukan seminggu sekali dalam 1 bulan pada pagi hari hingga siang hari. Variabel yang diukur in situ yaitu suhu, pH, dan DO sedangkan variabel yang diukur ex situ yaitu TSS, BOD, COD, dan Cr6+. Hasil pengukuran kualitas air variabel suhu 28,1 ̊C, TSS 23,33 mg/L, pH 6,23, DO 3,89 mg/L, BOD 2,2 mg/L, COD 26,58 mg/L, dan Cr6+ 0,02 mg/L. Hasil pengukuran kemudian dibandingan dengan baku mutu kelas II sesuai dengan Peraturan Pemerintah Republik Indonesia Nomor 22 Tahun 2021 Tentang Penyelenggaraan Perlindungan dan Pengelolaan Lingkungan Hidup. Penentuan status mutu air menggunakan metode IP pada stasiun I memenuhi baku mutu sedangkan stasiun II dan 3 tercemar. Penentuan status mutu air menggunakan metode CCME memiliki nilai 71,05 yang tergolong dalam kriteria cukup
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".