Sistem Deteksi Banjir Berbasis IoT Pada Sungai Abadi, Kec. Sei Bingai, Kab. Langkat
Bibliographic record
Abstract
Banjir merupakan bencana alam yang sering terjadi di wilayah Indonesia, termasuk di daerah Sungai Abadi, Kecamatan Sei Bingai, Kabupaten Langkat. Keterlambatan informasi mengenai potensi banjir sering kali menyebabkan kerugian yang besar, baik materiil maupun non-materiil. Untuk mengatasi hal tersebut, penelitian ini merancang dan mengimplementasikan sistem deteksi banjir berbasis Internet of Things (IoT) yang mampu memantau ketinggian air secara real-time dan memberikan peringatan dini kepada masyarakat. Sistem ini menggunakan sensor ultrasonik untuk mengukur ketinggian permukaan air sungai, mikrokontroler ESP32 sebagai pengendali utama, serta modul komunikasi yang terhubung ke jaringan internet untuk mengirimkan data ke server dan aplikasi pemantauan. Hasil pengujian menunjukkan bahwa sistem mampu bekerja secara stabil, memberikan pembacaan yang akurat, serta mengirimkan notifikasi peringatan ke pengguna saat ambang batas ketinggian air terlampaui. Diharapkan sistem ini dapat menjadi solusi efektif dalam mitigasi bencana banjir di wilayah rawan serta meningkatkan kesiapsiagaan masyarakat terhadap potensi bencana.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.015 |
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".