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Record W4403240007 · doi:10.29313/bcsurp.v4i3.14265

Identifikasi Kesesuaian Penerapan Kolam Retensi dan Kolam Detensi dalam Upaya Adaptasi Konsep Water Sensitive Urban Design pada Kawasan Peruntukan Industri Dayeuhkolot

2024· article· en· W4403240007 on OpenAlexaff
Teguh Tri Aryanto, Hani Burhanudin, Fachmy Sugih Pradifta

Bibliographic record

VenueBandung Conference Series Urban & Regional Planning · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicArchitectural and Urban Studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

Abstract. In the Bandung Regency Spatial Plan, 40.73% of the Dayeuhkolot District area has been designated as an industrial area. At least 352 industrial units have been operating, placing the Dayeuhkolot Industrial Area (KPI) as the area with the largest number of industrial units in Bandung Regency. However, the current condition of the Dayeuhkolot KPI is facing several major problems, including river flooding, critical air absorption, geohistorical issues of the Bandung Basin, unintegrated surface water management, waste management issues, and other geophysical problems that are quite common. This study aims to identify the suitability of the Water Sensitive Urban Design (WSUD) concept in the Dayeuhkolot District Industrial Area with the components tested being retention ponds and detention ponds. The analysis method used is overlay analysis consisting of intersect and superimpose techniques within the synoptic method design framework. The results of the study indicate that the WSUD components in the form of retention ponds and detention ponds have the potential to be applied in the area. These components can be used to retain and accommodate surface air. Retention area Placed in the northern part of the area that has a higher topography with the recommended component in the form of a detention pond. While the reservoir area in the south which is the lowest topography adjacent to the Citarum River can be recommended for the use of retention pond components. To combine the function of the components, it is recommended that land use patterns, drainage networks, and wastewater management schemes adapt the WSUD concept. Abstrak. Dalam RTRW Kabupaten Bandung telah ditetapkan 40,73% luas Kecamatan Dayeuhkolot sebagai kawasan peruntukan industri. Setidaknya 352 unit industri telah beroperasi hingga menempatkan Kawasan Peruntukan Industri (KPI) Dayeuhkolot sebagai kawasan dengan jumlah unit industri terbanyak di Kabupaten Bandung. Namun, kondisi KPI Dayeuhkolot saat ini menghadapi beberapa permasalahan utama antara lain banjir luapan sungai, kekritisan resapan air, isu geohistoris Cekungan Bandung, tidak terintegrasinya manajemen air permukaan, isu pengelolaan limbah, serta permasalahan geofisik lainnya yang cukup banyak ditemukan. Kajian ini bertujuan untuk mengidentifikasi kesesuaian konsep Water Sensitive Urban Design (WSUD) pada Kawasan Peruntukan Industri Kecamatan Dayeuhkolot dengan komponen yang diuji adalah kolam retensi dan kolam detensi. Metode analisis yang digunakan adalah analisis overlay yang terdiri dari teknik intersect dan superimpose dalam framework perancangan synoptic method. Hasil kajian menunjukan bahwa komponen WSUD berupa kolam retensi dan kolam detensi berpotensi untuk diterapkan di kawasan. Komponen tersebut dapat difungsikan kepada fungsi penahan dan penampung air permukaan. Area penahan ditempatkan pada bagian utara kawasan yang memiliki topografi yang lebih tinggi dengan komponen yang direkomendasikan berupa kolam detensi. Sedangkan area penampung berada pada sebelah selatan yang merupakan topografi paling rendah berdekatan dengan Sungai Citarum dapat direkomendasikan penggunaan komponen kolam retensi. Untuk memadukan fungsi komponen, maka direkomendasikan pola penggunaan lahan, jaringan drainase, serta skema pengelolaan air limbah yang mengadaptasi konsep WSUD.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.100
GPT teacher head0.250
Teacher spread0.150 · 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 teacher head, not a consensus.

Study designNot applicable
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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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