Produktivitas dan Strategi Pengembangan Budidaya Udang di Kawasan Teluk Banten, Serang Banten
Bibliographic record
Abstract
Udang merupakan komoditas perikanan unggulan yang mempunyai nilai ekonomis tinggi. Berbagai upaya dilakukan untuk meningkatkan produksinya, namun hasilnya masih fluktuatif dan salah satu penyebabnya adalah pemanfaatan lahan pertambakan yang belum optimal. Tujuan penelitian ini adalah untuk mengetahui produktivitas dan strategi pengembangan budidaya udang di pertambakan pesisir Teluk Banten. Lokasi penelitian di Pertambakan Pesisir Teluk Banten yang termasuk dalam wilayah administrasi Kota Serang dan Kabupaten Serang Provinsi Banten. Metode penelitian kuantitatif dengan analisa SWOT ( Strength, Weakness, Opportunity, Treath ) atau kekuatan, kelemahan, peluang dan ancaman. Pengambilan data melalui survei, focus group discussion , wawancara, kuesioner serta studi kepustakaan. Hasil penelitian menunjukkan produksi udang saat ini 525,78 ton/tahun. Regresi linier pada data 2020–2022 mempunyai persamaan y=73,03+292,59x dengan koefisien R 2 (0,9876). Interpretasinya adalah produksi cenderung bertambah 8,995 ton/ tahun. Analisa kesesuaian lahan menunjukkan potensi produksi udang sebesar 80.854 ton/tahun. Salah satu alternatif strategi peningkatan produktivitas yang mempunyai skor tinggi adalah strategi S-O dengan nilai 1,87 yaitu meningkatkan produksi budidaya udang dengan mengelola potensi luas lahan tambak dan lokasi yang strategis dengan menerapkan teknologi masa kini melalui hasil penelitian.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 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".