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Record W4415780532 · doi:10.24929/ft.v13i2.3532

ANALISIS PERBANDINGAN BIAYA DAN WAKTU PELAKSANAAN BETON HALF SLAB DENGAN BETON KONVENSIONAL PADA PROYEK REVITALISASI MUSEUM MANDALA MATHIKA SUBAK

2025· article· W4415780532 on OpenAlexaff
Ni Putu Desi Sukamdani Landuh, I Wayan Gde Erick Triswandana, I Gusti Agung Putu Eryani

Bibliographic record

VenueJurnal Ilmiah MITSU (Media Informasi Teknik Sipil Universitas Wiraraja) · 2025
Typearticle
Language
FieldHealth Professions
TopicOccupational Health and Safety Management
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPrecast concreteMandalaInformatics engineeringSlab

Abstract

fetched live from OpenAlex

Sistem pelaksanaan konstruksi pada proyek Revitalisasi Museum Mandala Mathika Subak keseluruhan pekerjaannya menggunakan metode cast in situ atau konvensional termasuk pada pekerjaan pelat lantai. Pada pelaksanaan pekerjaan pelat lantai dengan metode cast in situ membutuhkan waktu pengerjaan yang lama dan tenaga kerja yang banyak serta biaya yang dikeluarkan. Dalam pembangunan gedung pemilihan metode kerja sangat berpengaruh terhadap waktu pelaksanaan dan biaya. Pemilihan metode yang tepat akan membuat pembangunan menjadi efisien. salah satunya dengan penggunaan beton precast seperti half slab. Dalam penelitian ini akan membandingkan metode konvensional dan metode half slab dari aspek waktu pelaksanaan dan biaya, manakah yang lebih menguntungkan. Adapun hasil analisis perbandingan waktu pelaksanaan dan biaya dari perhitungan pelat cast in situ dan half slab pada proyek Revitalisasi Museum Mandala Mathika Subak diperoleh bahwa penggunaan metode half slab menghemat biaya 33% dari pada metode cast in situ dengan selisih biaya sebesar Rp. 552,730,037 dan dapat mempercepat waktu pelaksanaan 43% atau selama 19 hari.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0040.005
Science and technology studies0.0070.001
Scholarly communication0.0000.004
Open science0.0030.002
Research integrity0.0020.006
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 head0.034
GPT teacher head0.315
Teacher spread0.281 · 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 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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