Studi Optimasi Alokasi Air Irigasi Pada Daerah Irigasi Sarangan Kabupaten Madiun Dengan Program Dinamik
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Bibliographic record
Abstract
Abstrak Daerah Irigasi Sarangan merupakan salah satu daerah irigasi yang memiliki permasalahan ketersediaan air, yaitu air yang ada tidak cukup untuk memenuhi kebutuhan air irigasi. Berdasarkan permasalahan tersebut, dibutuhkan upaya pengelolaan air secara optimal dengan teknik optimasi. Teknik optimasi yang digunakan adalah program dinamik deterministik. Tujuan penelitian adalah mengoptimalkan alokasi air irigasi sehingga diperoleh keuntungan yang maksimum. Fungsi tujuan optimasi adalah keuntungan maksimum dengan fungsi kendala berupa luas lahan dan ketersediaan debit. Pola tata tanam yang digunakan sesuai dengan Rencana Tata Tanam Global (RTTG) periode 2021-2022. Berdasarkan hasil optimasi, diperoleh peningkatan luas tanam serta keuntungan pada Musim Tanam I sebesar 3%, pada Musim Tanam II sebesar 6%, dan pada Musim Tanam III sebesar 7%. Penelitian ini dapat bermanfaat bagi dinas terkait untuk mengetahui pemberian air optimal yang dapat menghasilkan keuntungan maksimum berdasarkan ketersediaan air yang ada pada Daerah Irigasi Sarangan. Kata kunci: optimasi, irigasi, program dinamik, deterministik Abstract The Sarangan Irrigation Area has a problem, namely that there is not enough water to meet irrigation water needs. Optimization techniques are needed based on these problems optimal water management efforts are required. This research aims to optimize irrigation water allocation to obtain maximum profit. The optimization technique used is a deterministic dynamic program. The optimization objective function is the maximum profit with the constraint functions in the form of land area and the availability of discharge. The cropping pattern follows the Global Planting Plan (RTTG) for 2021-2022. Based on the optimization results, it was found that the increase in planted area and profits in the first planting season was 3%, in the second planting season it was 6%, and in the third planting season it was 7%. This research can be useful for the relevant agencies to find out the optimal water supply that can produce maximum profit based on water availability in the Sarangan Irrigation Area. Keywords: optimization, irrigation, dynamic programming, deterministic
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it