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Record W3007933780 · doi:10.20527/bpi.v1i1.5

Analisis Kelayakan Desain Material Recovery Facility (Mrf) Dalam Pengelolaan Sampah Di Tpa Hutan Panjang Kota Banjarbaru

2018· article· id· W3007933780 on OpenAlexaff
Candra Yuliana

Bibliographic record

VenueBuletin Profesi Insinyur · 2018
Typearticle
Languageid
FieldEnvironmental Science
TopicWaste Management and Recycling
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsForestryEnvironmental sciencePhysicsGeography

Abstract

fetched live from OpenAlex

Pengelolaan sampah di Kota Banjarbaru masih belum maksimal. Hal ini terlihat dari sampah yang dikumpulkan di TPS kemudian diangkut dan ditimbun begitu saja di landfill TPA tanpa pengolahan. Perencanaan Material Recovery Facility (MRF) di TPA Hutan Panjang Gunung Kupang Kota Banjarbaru dapat digunakan sebagai salah satu alternatif untuk mengurangi volume sampah yang masuk ke TPA Hutan Panjang, menghemat kebutuhan lahan landfill, dan memperpanjang umur lahan TPA. Pengelolaan yang dilakukan adalah komposting sampah organik dengan sistem open windrow dan pemanfaatan kembali sampah anorganik yang mempunyai nilai jual. Komponen MRF yang dibutuhkan adalah lahan pemilahan, lahan penempungan sampah organik, lahan pencampuran sampah organik dengan EM4 (biostater), lahan pengomposan, tempat penyimpanan sampah kering (barang sortir), tempat penyimpanan kompos serta kantor administrasi. Lahan yang dibutuhkan untuk bangunan pengolahan sampah (MRF) adalah 12031,5 m2 dan rencana anggaran biaya yang diperlukan untuk biaya pembangunan dan penyediaan peralatan MRF sebesar Rp 10.563.047.380,00. Berdasarkan analisis kelayakan ekonomi dengan berbagai alternatif pembiayaan baik itu dengan pembiayaan dari pemerintah ataupun dengan pinjaman lunak menunjukkan pembangunan MRF ini layak untuk direalisasikan. Kata kunci: Analisa Ekonomi, Banjarbaru, Komposting, Material Recovery Facility

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.270
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.007

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 head0.018
GPT teacher head0.238
Teacher spread0.220 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2018
Admission routes1
Has abstractyes

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