ANALISIS KEHILANGAN AIR DENGAN METODE NERACA AIR DAN INFRASTRUCTURE LEAKAGE INDEX (ILI) PADA PERUMDA AIR MINUM KOTA SURAKARTA
Bibliographic record
Abstract
Air tidak berekening merupakan permasalahan utama yang dihadapi Perumda Air Minum Kota Surakarta, dengan tingkat kehilangan air mencapai 42,37% pada tahun 2023. Penelitian ini bertujuan untuk mengidentifikasi dan mengendalikan kehilangan air secara fisik dengan menggunakan metode neraca air dan Indeks Kebocoran Infrastruktur (Infrastructure Leakage Index). Data primer diperoleh melalui kunjungan lapangan dan pengukuran langsung, sedangkan data sekunder diperoleh dari Perusahaan Air Minum Kota Solo. Analisis neraca air dilakukan untuk mengetahui kehilangan air secara fisik, yang kemudian digunakan untuk menghitung ILI. Hasil penelitian menunjukkan volume penyaluran air sebanyak 24.270.430 meter kubik dan kehilangan air fisik sebanyak 9.669.609 meter kubik. Nilai ILI menggambarkan efektivitas pengelolaan jaringan distribusi dalam mengendalikan kehilangan air. Kesimpulan dari studi ini menyoroti pentingnya tindakan pengendalian bebas air dan mengurangi kebocoran/kehilangan air secara fisik untuk meningkatkan efisiensi dan kualitas operasional pelayanan air minum di Kota Surakarta.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.008 | 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".