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Record W3174052458 · doi:10.20527/jukung.v7i1.10809

STRATEGI SISTEM PENGELOLAAN AIR LIMBAH DOMESTIK DI KECAMATAN UJUNGBERUNG, CIBIRU, PANYILEUKAN, DAN CILEUNYI

2021· article· id· W3174052458 on OpenAlexaff
Fatinah Arina A'isyah

Bibliographic record

VenueJukung (Jurnal Teknik Lingkungan) · 2021
Typearticle
Languageid
FieldEngineering
TopicWetland Management and Conservation
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPhysicsEnvironmental scienceBusiness

Abstract

fetched live from OpenAlex

Sistem Pengelolaan Air Limbah Domestik (SPALD) harus diawali dengan penentuan strategi yang tepat, ditentukan mempertimbangkan kondisi wilayah perencanaan. Wilayah perencanan merupakan wilayah perbatasan antara Kota Bandung dan Kabupaten Bandung, sehingga berpotensi untuk dibangun SPALD regional meliputi 4 kecamatan, yaitu Kecamatan Ujungberung, Cibiru, Panyileukan di Kota Bandung, dan Kecamatan Cileunyi di Kabupaten Bandung. Saat ini SPALD Setempat (SPALDS) di Bandung Timur menggunakan sistem on site individual dan komunal, serta 11,04% masyarakat di Kabupaten Bandung masih membuang air limbah langsung ke sungai. Penelitian ini akan mengidentifikasi startegi SPALD menggunakan metode analisis Strenghts, Weakness, Opportunities, Threats (SWOT) kuantitatif. Parameter yang digunakan berdasarkan Pedoman Penyusunan Rencana Induk SPAL tahun 2016, parameter tersebut adalah kepadatan penduduk, topografi, resiko sanitasi, akses air minum, akses sanitasi layak, kawasan kumuh, permeabilitas tanah, kedalaman muka air tanah, pembiayaan daerah, kelembagaan pengelola air limbah, dan tingkat pendidikan. Hasil analisis SWOT, menunjukkan Kecamatan Panyileukan berada pada kuadran II (strategi selektif sistem terpusat), dengan arah pengembangannya dari SPALDS menjadi SPALDT kawasan. Sedangkan Kecamatan Ujungberung, Cibiru, dan Cileunyi berada pada kuadran III (strategi agresif sistem terpusat), dengan arah pengembangan SPALDT skala kota. Kata kunci: analisis SWOT, arah pengembangan, sistem pengelolaan air limbah domestik, strategi. The Domestic Wastewater Management System (SPALD) must begin with the determination of an appropriate strategy, determined by considering the conditions of the planning area. The planning area is the border area between Bandung City and Bandung Regency, so that the potential for regional SPALD to be built includes 4 districts, namely Ujungberung, Cibiru, Panyileukan in Bandung City, and Cileunyi District in Bandung Regency. Currently SPALD Local (SPALDS) in East Bandung uses individual and communal on site systems, and 11.04% of the people in Bandung Regency still dispose of their waste water directly into the river. This research will identify SPALD strategies using quantitative Strenghts, Weakness, Opportunities, Threats (SWOT) analysis methods. The parameters used are based on the 2016 SPAL Master Plan Preparation Guidelines, these parameters are population density, topography, sanitation risk, access to drinking water, access to proper sanitation, slum areas, soil permeability, groundwater level depth, regional funding, wastewater management institutions, and education level. The results of the SWOT analysis show Panyileukan sub-district is in quadrant II (centralized system selective strategy), with its development direction from SPALDS to regional SPALDT. Meanwhile, Ujungberung, Cibiru, and Cileunyi Subdistricts are in quadrant III (an aggressive strategy with a centralized system), with the direction of city-scale SPALDT development. Keywords: development direction, domestic wastewater management system, strategy, SWOT analysis.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0250.005

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.

Opus teacher head0.015
GPT teacher head0.222
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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