Pemodelan Building Information Modeling Bangunan Rumah Sakit Untuk Pengecekan Volume dan Bentrokan
Bibliographic record
Abstract
Digital konstruksi sudah menjadi kebutuhan dalam perencanaan dan pelaksanaan sebuah proyek, dan hal ini sudah menjadi keharusan untuk kelas bangunan tertentu, dengan munculnya Peraturan Pemerintah no. 16 tahun 2021. Perencana bangunan masih banyak yang mempergunakan cara konvensional, dengan menggunakan gambar CAD 2 dimensi, yang dimana cara ini banyak menghadapi permasalahan dalam pelaksanaan. Diantaranya ketidak sesuai volume dalam material dan pekerjaan tambah karena terjadinya benturan antar bidang atau disiplin. Penggunaan Building Information Modeling diharapkan dapat mengurangi permasalahan tersebut. Dalam penelitian ini perencanaan rumah sakit di daerah Madura, Jawa Timur yang telah selesai dilakukan perencanaan, dimodelkan ulang dengan menggunakan Autodesk Revit® 2020. Kemudian model tersebut diambil data untuk Bill of Quantity, serta dilaukan pengecekan Clash Detection, menggunakan perangkat lunak Autodesk Navisworks®. Hasilnya dapat diketahui bahwa terjadi selisih yang cukup besar untuk volume dan benturan yang cukup banyak. Maka degan menggunakan BIM dapat menghasilkan perhitungan volume yang lebih baik dan meminimalkan pekerjaan tambah saat pelaksanaan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".