SISTEM INFORMASI BENCANA TANAH LONGSOR (SI-BENAR) BERBASIS WEB UNTUK WILAYAH DESA CILILIN, KECAMATAN CILILIN, KABUPATEN BANDUNG BARAT
Bibliographic record
Abstract
Tanah longsor merupakan peristiwa kebencanaan yang kerap terjadi di wilayah Desa Cililin, Kecamatan Cililin, Kabupaten Bandung Barat, yang memiliki dampak negatif diantaranya adalah jatuhnya korban jiwa, kerugian rusaknya insfrastruktur, perekonomian yang tersendat, menurunnya harga tanah di daerah setempat serta trauma psikis bagi para korban selamat sehingga menimbulkan berbagai gangguan kejiwaan. Bencana tanah longsor dapat terjadi dimana dan kapan saja, namun dapat diidentifikasi lebih dini menggunakan Early Warning System (EWS) tanah longsor. EWS tanah longsor memerlukan suatu tampilan untuk menampilkan data-data monitoring kepada stakeholder yang berkaitan dengan bencana tanah longsor berupa sebuah aplikasi sistem informasi. Aplikasi sistem informasi bencana tanah longsor (Si-Benar) berbasis web ini dirancang menggunakan beberapa tahapan perancangan desain diagram, desain tampilan, menggunakan bahasa pemrograman PHP, basis data MySQL dan menampilkan data-data sensor dari hardware EWS tanah longsor dengan tampilan responsif. Sistem informasi bencana tanah longsor (Si-Benar) berbasis web merupakan solusi untuk memberikan informasi faktual mengenai data-data yang digunakan sebagai bahan monitoring dan evaluasi mengenai potensi bencana tanah longsor di wilayah Desa Cililin, Kecamatan Cililin, Kabupaten Bandung Barat. Kata kunci: Tanah Longsor, Early Warning System (EWS), Aplikasi sistem informasi bencana tanah longsor (Si-Benar), Data
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.034 |
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".