ASESMEN KUALITAS AIR PADA TAMBAK PEMBESARAN BENIH UDANG VANAME (Litopenaeus vannamei) BERDASARKAN ANALYTICAL HIERARCHY PROCESS DANWATER QUALITY INDEX
Bibliographic record
Abstract
Provinsi Lampung adalah penghasil udang terbesar di Indonesia. Salah satu kegiatan budidaya udang yang menentukan keberhasilan produksi udang adalah PT. Citra Larva Cemerlang yang menyediakan benih udang yang berkualitas, berlokasi di Kalianda, Lampung Selatan. Penelitian ini bertujuan untuk mengetahui kualitas air pada tambak pembesaran benih udang dengan pengelompokan parameter prioritas penting dalam penentuan kualitas air. Penelitian ini menggunakan metode deskriptif analitik. Data dalam penelitian ini diperoleh dari data pengamatan in situ, hasil analisis di Laboratorium Terpadu dan Sentra Inovasi Teknologi (LTSIT), hasil wawancara, observasi lapangan, dan pengisian kuesioner. Metode analisis data untuk mengetahui kualitas air adalah dengan membandingkan data hasil uji dengan baku mutu air. Dalam mengelompokkan parameter, digunakan Analytical Hierarchy Process (AHP) dan penilaian kualitas air dengan Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI). Hasil yang diperoleh pH (8,03-8,25), suhu (24,24-28,16 οC), salinitas (31,2-33,4%), dan oksigen terlarut (6,6-7,3 mg/L), kekeruhan (0-2,3 NTU), Total Suspended Solid (TSS) berkisar antara 0,33-0,46 mg/L, amonia (0-0,8 mg/L), fosfat (0,01-0,22 mg/L), nitrat (0,16-0,32 mg/L), Biological Oxygen Demand (BOD) berkisar antara 0,77-4,68 mg/L), dan logam berat (Cr, Cd, Cu, Pb, Zn, Ni) yang rata-rata nilainya melebihi baku mutu, hanya kandungan logam Cu tidak melebihi baku mutu air. Analisis parameter kualitas air menggunakan AHP dan CCME WQI menunjukkan bahwa sampel air memiliki kualitas air yang kurang baik. sehingga memerlukan perbaikan kualitas air. Perbaikan dapat dilakukan dengan cara meningkatkan aerasi, penggantian air secara rutin, pengangkatan lumpur, penggunaan probiotik dan pemantauan rutin terhadap parameter kualitas air untuk menjaga keseimbangan ekosistem. Kata kunci: Kualitas air, pembenihan, udang vaname, AHP, 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".