STUDI KINERJA IPAL DOMESTIK KOMUNAL WALLAGRI BANDUNG: EVALUASI PARAMETER FISIK-KIMIA DAN PERSPEKTIF PENGELOLA LAPANGAN
Bibliographic record
Abstract
Urbanisasi yang pesat meningkatkan beban pencemar air limbah domestik di perkotaan sehingga menuntut solusi pengolahan yang efektif, termasuk penggunaan instalasi pengolahan air limbah (IPAL) komunal. Penelitian ini bertujuan mengevaluasi kinerja IPAL Komunal Wallagri Bandung berdasarkan parameter fisik-kimia (pH, BOD₅, COD, TSS, amoniak, minyak & lemak, deterjen) dan menilai perspektif pengelola lapangan terkait operasi dan pemeliharaan. Pengambilan sampel dilakukan pada titik inlet dan outlet menggunakan teknik grab sampling, lalu dianalisis sesuai SNI 6989:2019 dan APHA (2023), kemudian dibandingkan dengan baku mutu Permen LH No. 11 Tahun 2025. Hasil pengujian menunjukkan pH berada pada kisaran netral, efisiensi penyisihan BOD₅ dan COD masing-masing 70,3% dan 64,8%, sedangkan TSS 57,7%. Meskipun terjadi penurunan signifikan, nilai BOD₅, COD, dan TSS masih melebihi baku mutu. Penurunan deterjen hanya 5,8%, menunjukkan perlunya penambahan unit filtrasi atau media adsorben. Hasil wawancara mengungkap keterbatasan pada pemeliharaan jaringan, pendanaan, dan kapasitas kelembagaan. Studi ini merekomendasikan optimasi operasi, edukasi masyarakat, peningkatan kapasitas kelembagaan, serta dukungan teknis dan finansial dari pemerintah untuk meningkatkan keberlanjutan IPAL komunal. Temuan ini dapat menjadi acuan peningkatan sistem sanitasi terdesentralisasi di kota-kota padat penduduk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".