Analisa Penyebab Keterlambatan Proyek Pembangunan Sidoarjo Town Square Menggunakan Metode Fault Tree Analysis (FTA)
Bibliographic record
Abstract
Setiap proyek konstruksi pada umumnya mempunyai rencana pelaksanaan dan jadwal pelaksanaan tertentu, kapan pelaksanaan proyek tersebut harus dimulai, kapan proyek tersebut harus diselesaikan, bagaimana proyek tersebut akan dikerjakan, serta bagaimana penyediaan sumber dayanya. Diharapkan dalam pelaksanaanya tidak terjadi keterlambatan karena keterlambatan yang terjadi akan mengakibatkan meningkatnya biaya proyek. Namun, dalam pelaksanaan proyek pembangunan Sidoarjo Town Square mengalami keterlambatan. Metode yang direncanakan dalam pembahasan untuk mengetahui faktor yang mempengaruhi terjadinya keterlambatan yaitu Metode Fault Tree Analysis (FTA) dan Method Obtain Cut Set (MOCUS). Didapatkan bahwa item pekerjaan yang mengalami keterlambatan yaitu: pekerjaan struktur GWT STP, pekerjaan finishing fasade dan canopy, dan pekerjaan atap. Dari hasil analisa FTA dari ketiga top event, didapatkan bahwa keterlambatan terjadi dikarenakan perubahan desain serta perijinan, dimana keduanya akibat faktor penyebab keterlambatan dari pihak owner.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".