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Record W4392465543 · doi:10.47600/jtst.v5i3.692

Implementasi Beton Precast dengan Konsep Green Building pada Pembangunan Rumah Literasi untuk Masyarakat

2023· article· id· W4392465543 on OpenAlexaff
Putri Marza Nabila Anuna, Novia Tamasiro, Victor Mundui, Stefani Peginusa

Bibliographic record

VenueJurnal Teknik Sipil Terapan · 2023
Typearticle
Languageid
FieldEngineering
TopicGeotechnical and construction materials studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPrecast concreteCivil engineeringEngineering

Abstract

fetched live from OpenAlex

Artikel ini menjelaskan tentang implementasi beton precast menggunakan konsep green building yang diterapkan pada pembangunan rumah literasi. Penelitian ini bertujuan untuk mendesain bangunan menggunakan beton precast dengan konsep green building, menghitung rencana anggaran biaya, dan menganalisis waktu pekerjaan bangunan rumah literasi. Metode penelitian yang digunakan adalah eksperimental laboratorium computer dengan menggunakan bantuan software Autocad untuk desain dan Microsoft Excel untuk perhitungan biaya dan waktu. Konsep green building yang diterapkan dalam pembangunan rumah literasi yaitu penggunaan kaca low-e, dan penggunaan lampu automatic motion sensor. Bangunan ini memiliki luas 144m2. Hasil dari penelitian ini total rencana anggaran biaya keseluruhan yaitu Rp529.059.015. Sedangkan waktu pekerjaan didapatkan 66 hari atau 3 bulan. Pada penelitian ini diharapkan luaran yang menunjukkan bahwa penggunaan beton precast dalam pembangunan adalah pilihan yang lebih disarankan. Hal ini dapat mengurangi anggaran biaya, mengurangi dampak lingkungan, meningkatkan efisiensi konstruksi, dan mencapai tujuan pembangunan berkelanjutan.
 
 Kata kunci: rumah literasi, beton precast, green building

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.019
GPT teacher head0.254
Teacher spread0.235 · 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 designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

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