Identifikasi Karakteristik Desain Temporary Modular Shelter pada Bencana di Indonesia melalui Nvivo dan Review Literatur
Bibliographic record
Abstract
Kurangnya pemahaman sistematis mengenai faktor-faktor utama yang memengaruhi desain Temporary Modular Shelter (TMS) dalam berbagai konteks kebencanaan menjadi tantangan dalam pengembangan hunian darurat yang efektif. Penelitian ini bertujuan untuk mengidentifikasi karakteristik desain TMS yang paling sering dibahas dalam literatur ilmiah internasional. Metode yang digunakan adalah systematic literature review terhadap 120 artikel yang diperoleh dari ScienceDirect, SpringerLink, dan Google Scholar. Artikel yang memenuhi kriteria inklusi dianalisis menggunakan perangkat lunak NVivo 12 melalui pendekatan thematic coding untuk mengevaluasi tema dan indikator yang paling dominan. Hasil penelitian menunjukkan bahwa kemudahan akses ke lokasi bencana (188), tingkat keterampilan tenaga kerja (178), dan logistik material (175) merupakan tiga tema dengan frekuensi tertinggi. Sebaliknya, dimensi bangunan hanya muncul sebanyak 120 kali. Temuan ini menegaskan bahwa aspek sumber daya manusia dan logistik lebih krusial dibandingkan spesifikasi teknis bangunan dalam konteks perancangan TMS. Penelitian ini berkontribusi terhadap pengembangan desain hunian darurat yang lebih adaptif, efisien, dan berbasis bukti dalam bidang teknik sipil dan perencanaan tanggap bencana.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
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".