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Record W4386541873 · doi:10.29313/bcsurp.v3i2.8673

Kajian Pengendalian Penggunaan Lahan di Kecamatan Cimenyan

2023· article· en· W4386541873 on OpenAlexaff
Muhammad Farhan, Yulia Asyiawati

Bibliographic record

VenueBandung Conference Series Urban & Regional Planning · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsLandslideGeographyForestryLand useHuman settlementPopulationCivil engineeringEngineeringGeotechnical engineeringArchaeology

Abstract

fetched live from OpenAlex

Abstract.Cimenyan District in the development of land use is still not controlled causing disruption of the protective function. so that it can lead to landslides. The occurrence of landslides from time to time is of course a special concern regarding the area which is prone to landslides. On the other hand, Cimenyan Sub-District has a relatively high population among other sub-districts in Bandung Regency, of course the occurrence of this disaster can not only cause material losses but can also claim lives. From this study aims to identify the level of deviation and identify the suitability of land use control instruments in Cimenyan District. In carrying out this analysis, it was carried out using the overlay method of land use maps, spatial patterns, and data on the level of vulnerability to landslides from KRB Bandung Regency. From these results, land changes from 2013 - 2022 have increased the area of agriculture and settlements, while there is a level of deviation. Large land use deviations are quite high, namely, 7.82% or the equivalent of 353.70 Ha. To identify the causes of landslides due to high rainfall and reduced plant vegetation with strong roots. and identification of control instruments can be done by overlaying a map of land irregularities and the level of vulnerability to landslides, as for the recommendations from the control, of course, first verify the legality of the permit. If there is no legality/permit, of course, sanctions/disincentives must be given. Abstrak. Kecamatan Cimenyan dalam perkembangan penggunaan lahan masih belum terkendali menimbulkan gangguan fungsi lindung. sehingga dapat mengakibatkan bencana tanah longsor Terjadinya bencana tanah longsor dari waktu ke waktu tentu dapat menjadi perhatian khusus terkait wilayah tersebut yang rawan akan bencana longsor. Di sisi lain Kecamatan Cimenyan memiliki jumlah penduduk cukup tinggi diantara kecamatan lainnya di Kabupaten Bandung, tentunya terjadinya bencana tersebut tidak hanya dapat memberikan kerugian berupa materiel saja melainkan dapat merenggut korban jiwa. Dari penelitian tersebut bertujuan untuk mengidentifikasi tingkat penyimpangan dan mengidentifikasi kesesuaian instrumen pengendalian penggunaan lahan di Kecamatan Cimenyan. Dalam melakukan analisis ini dilakukan dengan menggunakan metode overlay peta penggunaan lahan, pola ruang, dan data tingkat kerentanan bencana tanah longsor dari KRB Kabupaten Bandung. Dari hasil tersebut perubahan lahan dari tahun 2013 - 2022 terjadi penambahan luasan pada pertanian dan permukiman sedangkan untuk adanya tingkat penyimpangan Penggunaan lahan besar penyimpangan cukup tinggi yaitu, 7,82% atau setara dengan 353,70 Ha. Untuk identifikasi penyebab terjadinya tanah longsor diakibatkan karena tingginya curah hujan dan berkurangnya vegetasi tanaman berakar kuat. dan untuk identifikasi instrumen pengendalian dapat dilakukan dengan overlay peta penyimpangan lahan dan tingkat kerentanan tanah longsor, adapun dari rekomendasi dari pengendalian tersebut tentuntunya verifikasi terlebih dahulu legalitas izinnya Apabila tidak ada legalitas/izinnya maka tentu harus diberikan sanksi/disinsentif.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.061
GPT teacher head0.242
Teacher spread0.182 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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