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Record W4395093105 · doi:10.29313/bcsurp.v4i1.11657

Pengkajian Risiko Bencana Longsor di Kecamatan Cimanggung Kabupaten Sumedang

2024· article· en· W4395093105 on OpenAlexaff
Jody Oskar Rahmanizar, Ivan Chofyan, Irland Fardani

Bibliographic record

VenueBandung Conference Series Urban & Regional Planning · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies
Canadian institutionsEncana (Canada)
FundersUniversitas Islam Bandung
KeywordsPhysics

Abstract

fetched live from OpenAlex

Abstract. The district of Cimanggung has been consistently affected by floods and landslides over the years, with 22 flood incidents and 10 landslide incidents recorded in the last five years. It is known that Cimanggung is a diverse district with various Spatial activities, including residential living, agriculture, trade, and significant industrial activities. The high frequency of floods and landslides, coupled with the concentration of various activities that exist in the district, can result in substantial losses for the local population and external communities with interests in the area. Currently, there is no mapping of the risk assessment for flood and landslide disasters in the district. Therefore, there is a need for a comprehensive risk mapping study for floods and landslides, aiming to establish a foundation for disaster risk mitigation. The research adopts a quantitative approach, relying on secondary data and existing field information. The analysis process follows the guidelines of PERKA BNPB No.2 of 2012, utilizing the Geomorphological Flood Assessment method to determine flood hazards and employing an overlay method with weighting variables based on Puslittanak, 2004, to assess landslide hazards. The results of the analysis indicate that the multi-disaster risk classes for floods and landslides in Cimanggung are in the Moderate class. The largest high-risk area is found in Cimanggung Village with an area of 637.98 hectares. Meanwhile, the highest low-risk class is in Tegalmanggung Village, covering an area of 177.3 hectares. Abstrak. Kecamatan Cimanggung merupakan kecamatan yang dari tahun ke tahun selalu dilanda bencana banjir dan longsor, dimana selama lima tahun terakhir terjadi 22 kejadian banjir dan 10 kejadian longsor. Diketahui juga bahwa Kecamatan Cimanggung merupakan kecamatan dengan aktivitas kegiatan yang beragam, mulai dari kegiatan bermukim, pertanian, perdagangan, juga aktivitas industri besar terdapat di kecamatan. Tingginya frekuensi terjadinya bencana longsor dan banjir, beserta dengan konsentrasi kegiatan masyarat yang tinggi dapat menyebabkan kerugian yang besar bagi penduduk setempat, dan masyarakat luar wilayah yang memiliki kepentingan dalam wilayah. Di wilayahnya sendiri belum adanya pemetaan kajian risiko bencana banjir dan longsor di kecamatan, Oleh karena itu perlu adanya kajian pemetaan risiko bencana banjir dan longsor, dengan harapan dapat menjadi basis dalam mitigasi risiko bencana banjir dan longsor. Pendekatan yang digunakan dalam penelitian ini bersifat kuantitatif, mengandalkan data sekunder dan informasi lapangan yang sudah ada. Proses analisis mengacu pada PERKA BNPB No.2 Tahun 2012 dan menggunakan metode Geomorphical Flood Assesment dalam mencari tahu bahaya Banjir wilayah, dan mengunakan metode overlay dengan variabel pembobotan berdasarkan Puslittanak, 2004 dalam mencari tahu bahaya Longsor wilayah. Hasil dari analisis menunjukan bahwa kelas risiko multi bencana banjir dan longsor di Kecamatan Cimanggung berada pada kelas Sedang. dimana wilayah dengan luasan terbesar kelas tinggi terdapat pada Desa Cimanggung dengan luasan 637,98 Ha. Sedangkan kelas risiko rendah tertinggi terdapat pada Desa Tegalmanggung dengan luasan 177.3 Ha.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.041
GPT teacher head0.233
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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