PENERAPAN BUILDING INFORMATION MODELING (BIM) PADA TAHAP KESIAPSIAGAAN BENCANA ALAM DI INDONESIA
Bibliographic record
Abstract
Indonesia adalah negara kepulauan yang terbesar di dunia yang sangat berpotensi terjadi bencana alam. Dengan mengerahkan semua daya upaya baik personil dan materiil serta teknologi seperti Building Information Modeling (BIM) diharapkan dapat meminimalisir dampak bencana berupa kerugian materiil dan korban jiwa. Tulisan ini membahas secara sistematis tentang penerapan BIM untuk memperkuat kesiapsiagaan bencana alam di Indonesia. Metode yang digunakan dalam penulisan ini adalah kualitatif dengan teknik pengumpulan data dilakukan secara dokumentasi atau studi literatur baik melalui buku, media elektronik serta sosial media yang disesuaikan dengan tema penelitian. Dari penelitian ini didapat peran BIM dalam kesiapsiagaan antara lain pemetaan dan analisis risiko, perencanaan evakuasi dan shelter, perencanaan infrastruktur darurat, simulasi bencana dan respons, serta koordinasi dan kolaborasi antar pemangku kepentingan. Sedangkan tantangan dalam penerapan BIM adalah keterbatasan data geospasial, mahalnya biaya investasi infrastruktur IT dan pelatihan BIM, keterbatasan Sumber Daya Manusia (SDM) yang terampil atau tenaga ahli BIM serta kurangnya integrasi dengan sistem dan standar yang ada. Dengan terus meningkatkan kapasitas serta evaluasi dari studi kasus negara lain seperti Jepang diharapkan Indonesia dapat lebih siap menghadapi dan merespons bencana alam khususnya dengan penggunaan teknologi BIM.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".