ANALISIS TINGKAT KERAWANAN BENCANA BANJIR DANLONGSOR DI KABUPATEN PESISIR BARAT, PROVINSI LAMPUNG
Bibliographic record
Abstract
Bencana alam adalah kejadian yang dapat mengakibatkan kerusakan alam, kerusakan sarana prasarana, korban jiwa, kehilangan harta benda, serta terganggunya kegiatan manusia. Secara topografi pesisir barat berapa pada bagian pinggir pulau Sumatra tepatnya pantai barat Provinsi Lampung, dan secara topologi berbentuk perbukitan antara ketinggian 600 sampai dengan 1.000 mdpl dan curah hujan per tahun di rata-rata 2.500 sampai dengan 3.000 mm/tahun sehingga rentan akan terjadinya bencana banjir dan longsor. Penelitian ini bertujuan menganalisis tingkat kerawanan bencana banjr dan longsor di wilayah Kabupaten Pesisir Barat, Provinsi Lampung. Analisis tingkat kerawanan bencana banjir dan longsor menggunakan parameter penggunaan lahan yang dihasilkan dari klasifikasi menggunakan Support Vector Machine (SVM), pada parameter gempa dan curah hujan dilakukan interpolasi dengan metode Inverse Distance Weighted (IDW). Bencana banjir parameter yang digunakan ialah kemiringan lereng, ketinggian lahan, curah hujan, penggunaan lahan, dan jenis tanah, sedangkan bencana longsor digunakan parameter curah hujan, gempa, jenis tanah, jenis batuan, kemiringan lereng, dan penggunaan lahan. Penentuan bobot masing-masing parameter menggunakan metode Analytic Hierarchy Process (AHP), dan kemudian dilakukan overlay dengan Intersect. Hasil dari overlay dengan intersect menunjukkan bahwa tingkat kerawanan bencana banjir di Kabupaten Pesisir Barat ialah tinggi dengan luas wilayah 1.628,47 km2 atau 56% dari seluruh luas wilayah Kabupaten Pesisir Barat, sedangkan bencana longsor memiliki tingkat kerawanan longsor yang rendah dengan luas wilayahnya 1.949,783 km2 atau 67% dari seluruh luas wilayah Kabupaten Pesisir Barat. Kata Kunci: Banjr dan Longsor, Support Vector Machine, Analytic Hierarchy Process Natural disasters cause various damages, including the destruction of infrastructure, loss of life, loss of property, and disruption of human activities. Geographically, the west coast of Sumatra Island, specifically in Lampung Province, consists of hills ranging from 600 to 1,000 meters above sea level, with an annual rainfall averaging between 2,500 and 3,000 mm/year, making it vulnerable to floods and landslides. This study aims to analyze the vulnerability to floods and landslides in the Pesisir Barat Regency area, Lampung Province.The analysis of the floods and landslides vulnerability includes parameters such as land use derived from classification using a Support Vector Machine (SVM), and interpolation of earthquake and rainfall parameters using the Inverse Distance Weighted (IDW) method. The flood vulnerability assessment includes slope, elevation, rainfall, land use, and soil type, while landslide vulnerability assessment considers rainfall, earthquake, soil type, rock type, slope, and land use parameters. The weight of each parameter is determined using the Analytic Hierarchy Process (AHP) method, followed by overlay analysis using the Intersect method. The study findings indicate that the to flood disasters in Pesisir Barat Regency covers a high-risk area of 1,628.47 km2, accounting for 56% of the total area, whereas the vulnerability to landslides is lower, affecting 1,949.783 km2 or 67% of the total area of Pesisir Barat Regency. Keywords: Flood and Landslide, Support Vector Machine, Analytic Hierarch Process
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".