ANALISIS KINERJA PENGELOLAAN AIR HUJAN DENGAN SISTEM PEMANENAN AIR HUJAN DAN INFILTRATION TRENCH DI PERUMAHAN DOSEN UNSRI KELURAHAN BUKIT LAMA
Bibliographic record
Abstract
Perubahan tata guna lahan mempengaruhi jumlah runoff yang dapat meresap ke dalam tanah. Permasalahan ini dapat ditemukan di Perumahan Dosen UNSRI. Hal ini dapat diatasi dengan pengelolaan air dari hujan seperti Infiltration Trench (parit infiltrasi) maupun Rain Water Harvesting (permanen air hujan. Penelitian ini bertujuan untuk menganalisis scenario terbaik dalam pengolahan air hujan di daerah penelitian untuk mengurangi runoff berlebih yang terjadi. Data curah hujan harian, tata guna lahan, premeabilitas tanah dan pengukuran lahan serta dimensi drainase existing digunakan dalam analisis algoritma. Penentuan skenario efektif dilakukan dengan analisis algoritma serta dengan memperhitungkan RAB. Skenario yang meliputi infiltration trench kedalaman 1 m serta sistem PAH dengan volume tangki 2 m3 memiliki efektivitas rata-rata terbesar yaitu 55,33%, apabila dibanding dengan skenario Infiltration Trench kedalamam 1 m saja, dapat dikatakan bahwa sistem PAH membawa efektivitas sebesar 23,94%. Sebaliknya dengan penambahan pada implementasi Infiltration Trench hanya meningkatkan efektivitas sebesar 18,4%. Rencana Anggaran Biaya total pada implementasi Infiltration Trench ialah 2 kali lipat dari Sistem PAH. Maka ditetapkan skenario terbaik yaitu implementasi Sistem PAH dengan volume tangki 2 m3. Skenario ini memiliki nilai efektivitas pengurangan runoff rata-rata sebesar 37,05% dari 3 data tahun yang digunakan dan biaya sebesar 1,8 Miliyar Rupiah untuk keseluruhan 194 rumah.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".