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Record W4415780533 · doi:10.24929/ft.v13i2.3574

PERENCANAAN PELAKSANAAN PROYEK KONSTRUKSI KANTOR KPU KABUPATEN BADUNG

2025· article· W4415780533 on OpenAlexaff
I Gusti Ayu Dina Pramesti, I Wayan Jawat, 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
KeywordsInformatics engineering

Abstract

fetched live from OpenAlex

Perencanaan pelaksanaan proyek konstruksi merupakan tahapan penting yang bertujuan untuk mengoptimalkan waktu, biaya, dan mutu pekerjaan. Dalam proyek pembangunan Gedung Kantor KPU Kabupaten Badung, perencanaan pelaksanaan menjadi sangat krusial mengingat lokasi proyek yang berada di kawasan padat permukiman, sehingga diperlukan metode pelaksanaan yang tepat dan efisien. Permasalahan yang dihadapi adalah bagaimana menyusun jadwal pelaksanaan, menghitung kebutuhan sumber daya, serta merencanakan biaya secara sistematis agar proyek dapat berjalan sesuai rencana. Penelitian ini menggunakan metode Precedence Diagram Method (PDM) untuk menyusun penjadwalan proyek dan Microsoft Project sebagai alat bantu visualisasi. Selain itu, dilakukan analisis kebutuhan sumber daya manusia, bahan, dan alat serta penyusunan Rencana Biaya Pelaksanaan (RBP) dan Rencana Anggaran Biaya (RAB). Hasil penelitian menunjukkan bahwa perencanaan pelaksanaan dengan metode PDM menghasilkan jadwal proyek yang terstruktur dan efisien. Total biaya yang diperoleh dari hasil analisis RBP adalah sebesar Rp 4,159,270,327.34, sedangkan RAB mencapai Rp 4,623,397,141.26, dengan selisih biaya sebesar Rp 464,126,813.93. Penelitian ini diharapkan menjadi acuan perencanaan pelaksanaan proyek konstruksi bangunan serupa, khususnya dalam konteks penggunaan metode PDM.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.621
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0060.001
Scholarly communication0.0000.003
Open science0.0030.002
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.002

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 head0.036
GPT teacher head0.329
Teacher spread0.294 · 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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