PENENTUAN STATUS MUTU DAN STRATEGI PENGENDALIAN PENCEMARAN AIR SUNGAI SEBAGAI UPAYA PENGELOLAAN KUALITAS LINGKUNGAN(Studi Kasus: Sungai Rambut, Kabupaten Pemalang-Tegal, Jawa Tengah)
Bibliographic record
Abstract
ABSTRAK Penentuan Status Mutu dan Strategi Pengendalian Pencemaran Air Sungai Sebagai Upaya Pengelolaan Kualitas Lingkungan (Studi Kasus: Sungai Rambut, Kabupaten Pemalang-Tegal, Jawa Tengah) Aaf Efiana, Dwi Siwi Handayani, Winardi Dwi Nugraha Sungai Rambut merupakan bagaian dari DAS Rambut yang terletak di perbatasan antara Kabupaten Pemalang dan Kabupaten Tegal, Provinsi Jawa Tengah. Hulu sungai utama berada di Desa Kajenengan, Kecamata Bojong dan hilir berada di Desa Kedungkelor Kecamatan Warureja. Berdasarkan tata guna lahan, di sepanjang sungai Rambut didominasi oleh lahan pertanian, perkebunan dan pemukiman. Adanya aktifitas di sekitar Sungai Rambut dapat menurunkan kualitas air karena masuknya air limbah seperti limbah domestik dan limbah pertanian ke sungai. Penelitian ini bertujuan untuk menentukan status mutu air sungai dengan menggunakan metode NSF WQI (Nation Sanitation Foundation Water Quality Index) dan CCME WQI (Canadian Council of Ministers of The Environment Water Quality Index). Parameter yang diukur yaitu Temperatur, Kekeruhan, Total Solid, pH, Phospat, DO, BOD, Nitrat, dan Fecal Coliform. Hasil perhitungan status mutu air dengan metode NSF WQI adalah Sungai Rambut masuk dalam kategori “Sedang-Baik” dengan kisaran nilai 63,83-74,15, sedangkan hasil perhitungan metode CCME WQI, status mutu Sungai Rambut adalah “Buruk-Sangat Baik” dengan kisaran nilai 53,54-100. Pengendalian pencemaran air Sungai Rambut dilakukan dengan berdasarkan analisis kualitas air, hasil status mutu air, tata guna lahan, studi literatur. Kata kunci: kualitas air, Sungai Rambut, NSF WQI, CCME WQI
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".