Penentuan Lokasi Perumahan Pasca Bencana Berdasarkan Preferensi Masyarakat Di Kota Palu
Bibliographic record
Abstract
Penelitian ini bertujuan untuk menentukan lokasi perumahan pasca bencana diantara perumahan Bukit Malontara Wahbah Residence, Perumahan Petobo Residence, Perumahan Layana View Residence dan Perumahan D’Grand Pearl Land, Kota Palu berdasarkan preferensi masyarakat. Sasaran penelitian meliputi menganalisis dan mengidentifikasi preferensi dominan masyarakat dalam menentukan lokasi perumahan pasca bencana serta menentukan lokasi perumahan yang strategis pasca bencana sesuai preferensi masyarakat. Penelitian ini menggunakan metode penelitian kuantitatif dengan sumber data primer dan sekunder yang dikumpulkan menggunakan metode kuesioner, wawancara, observasi dan dokumentasi. Data diolah menggunakan analisis hirarki proses. Berdasarkan hasil analisis hirarki proses, ditunjukkan bahwa preferensi masyarakat menentukan lokasi perumahan pasca bencana didominasi kecenderungan terhadap kriteria aksesibilitas dengan nilai 26,1%, sedangkan lokasi yang strategis untuk bertempat tinggal pasca bencana di Kota Palu sesuai kepentingan prioritas yaitu, perumahan Bukit Malontara Wahbah Residence. Kesimpulan dari hasil penelitian adalah lokasi perumahan yang ditentukan masyarakat cenderung berdasarkan aspek aksesibilitas atau kemudahan pencapaian pusat-pusat kegiatan, rekomendasi dari penelitian ini yaitu Perumahan Bukit Malontara Wahbah Residence di Kecamatan Tatanga, Kota Palu dengan nilai prioritas sebesar 31,3%.
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 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 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".