KAJIAN KERENTANAN SOSIAL TERHADAP BENCANA BANJIR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACTFlood is a natural phenomenon that occurs due to high rainfall intensity which causes excess water that is not accommodated by the drainage network of an area (Rachmat & Pamungkas, 2014). Based on the 2015 BNPB disaster risk assessment in (BNPB, 2016), the number of people exposed to flood risk in all regions of Indonesia is more than 170 million people with an exposed asset value of more than IDR 750 trillion. Floods are disasters that always occur every year in several places. The composition of the population greatly affects the level of social vulnerability to floods. Therefore, this research needs to be carried out with the aim of identifying social vulnerability to flood disasters as one of the disaster management efforts to reduce disaster risk.The method used in this research is qualitative method with a literature review approach. The results showed that the level of social vulnerability in Baleendah District, East Tondano District, and the coastal villages of Demak Regency is influenced by several factors. These factors are population, population according to sex, population according to age group, population density, poverty level, population with disabilities, level of dependency, number of family members, population growth, education level, and health insurance.Keywords: Social Vulnerability, Flood Disaster, Vulnerability Factors ABSTRAKBanjir adalah fenomena alam yang terjadi akibat intensitas curah hujan yang tinggi yang menyebabkan kelebihan air yang tidak tertampung oleh jaringan pematusan suatu wilayah (Rachmat & Pamungkas, 2014). Berdasarkan kajian risiko bencana BNPB tahun 2015 dalam (BNPB, 2016), jumlah jiwa terpapar risiko bencana banjir di seluruh wilayah Indonesia yaitu lebih dari 170 juta jiwa dengan nilai aset terpaparnya lebih dari Rp750 triliun. Banjir merupakan bencana yang selalu terjadi setiap tahun di beberapa tempat. Komposisi penduduk sangat mempengaruhi tingkat kerentanan sosial terhadap bencana banjir. Oleh karena itu, penelitian ini perlu dilakukan dengan tujuan untuk mengidentifikasi kerentanan sosial terhadap bencana banjir sebagai salah satu upaya penanggulangan bencana untuk mengurangi risiko bencana.Metode yang digunakan dalam penelitian ini yaitu metode kualitatif dengan pendekatan kajian literatur. Hasil penelitian menunjukkan bahwa tingkat kerentanan sosial di Kecamatan Baleendah, Kecamatan Tondano Timur, dan pedesaan pesisir Kabupaten Demak dipengaruhi oleh beberapa faktor. Faktor-faktor tersebut yaitu jumlah penduduk, penduduk menurut jenis kelamin, penduduk menurut kelompok umur, kepadatan penduduk, tingkat kemiskinan, penduduk penyandang disabilitas, tingkat ketergantungan, jumlah anggota keluarga, pertumbuhan penduduk, tingkat pendidikan, dan jaminan kesehatan.Kata kunci: Kerentanan Sosial, Bencana Banjir, Faktor Kerentanan
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it