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Record W4403239180 · doi:10.29313/bcsurp.v4i3.15676

Analisis Biaya Dampak Lingkungan Pembangunan RS Salman Hospital Tahap Pra-Konstruksi, Konstruksi dan Operasional

2024· article· en· W4403239180 on OpenAlexaff
Adam Badi Albar, Yulia Asyiawati, Tonny Judiantono

Bibliographic record

VenueBandung Conference Series Urban & Regional Planning · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsChemistryBusiness

Abstract

fetched live from OpenAlex

Abstract. The development of a region has an impact on increasing population and the need for community service facilities, one of which is health facilities. Fulfillment of health facilities in Soreang District will be the construction of the Salman Hospital located in Sekarwangi Village. The construction of the hospital has impacts that have implications for environmental costs. The aim of this research is to identify the value of environmental costs arising from the impact of development from the Geo-Physical-Chemical aspect. The approach method in this research is environmental impact cost analysis (ABDL). The analytical method used is economic valuation. The findings from this research are that the initial baseline is a rice field farming area with an environmental value of IDR 454,424,054.51. Furthermore, the impacts caused during the construction phase include base camp operations, mobilization of tools and materials, and earthworks. The environmental value during the construction period was -Rp 620,903,153.52. Meanwhile, at the operational stage, the impact on the environment is in the form of type C hospital operations and maintenance of supporting facilities and infrastructure with an impacted environmental value of -Rp. 202,036,555.36 Based on these conditions, it can be concluded that the total environmental value of the construction of this hospital is -Rp. 368,515. ,654.37, which shows that development has a negative impact on the environment. The recommendation in this research is that owners must pay more attention to the potential impact of costs on the environment from new development in order to reduce negative impacts. Abstrak. Perkembangan suatu wilayah memberikan dampak terhadap peningkatan jumlah penduduk dan kebutuhan sarana pelayanan masyarakat, salah satunya adalah sarana kesehatan. Pemenuhan sarana kesehatan di Kecamatan Soreang akan dibangunnya Rumah Sakit Salman Hospital yang berlokasi di Desa Sekarwangi. Pembangunan rumah sakit tersebut menimbulkan dampak yang berimplikasi terhadap biaya lingkungan. Tujuan penelitian ini adalah mengidentifikasi nilai biaya lingkungan yang ditimbulkan dari dampak pembangunan dari aspek Geo-Fisik-Kimia. Metode pendekatan pada penelitian ini yaitu analisis biaya dampak lingkungan (ABDL). Metode analisis yang digunakan ialah valuasi ekonomi. Temuan dari penelitian ini ialah rona awal berupa kawasan pertanian sawah dengan nilai lingkungan sebesar Rp 454.424.054,51. Selanjutnya, dampak yang ditimbulkan pada tahap konstruksi diantaranya pengoperasian base camp, mobilisasi alat dan material, dan pekerjaan tanah. Nilai lingkungan pada masa konstruksi sebesar -Rp 620.903.153,52. Sedangkan pada tahap operasional, dampak terhadap lingkungan berupa operasional rumah sakit tipe C dan pemeliharaan sarana dan prasarana penunjang dengan nilai lingkungan terdampak sebesar -Rp 202.036.555,36 Berdasarkan kondisi tersebut dapat disimpulkan bahwa total nilai lingkungan dari pembangunan rumah sakit ini sebesar -Rp 368.515.654,37, yang menunjukkan bahwa pembangunan memberikan dampak negatif terhadap lingkungan. Rekomendasi dalam penelitian ini adalah pemilik atau owner harus lebih memperhatikan potensi dampak biaya yang ditimbulkan terhadap lingkungan dari pembangunan baru agar dapat mengurangi dampak negatif.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.026
GPT teacher head0.245
Teacher spread0.219 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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