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Record W4403239152 · doi:10.29313/bcsurp.v4i3.14036

Analisis Variabel yang Berpengaruh terhadap Konsolidasi Lahan di Permukiman Kumuh Kawasan Perkotaan Kelurahan Ciumbuleuit, Kota Bandung

2024· article· en· W4403239152 on OpenAlexaff
Chereen Haura Puti Aji, Nia Kurniasari

Bibliographic record

VenueBandung Conference Series Urban & Regional Planning · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWater and Land Management
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

Abstract. The fulfillment of land needs is one step towards achieving social welfare for all Indonesian people, which is part of the government's responsibility. The difference between communities with adequate housing and those living in slums highlights a social gap that should not exist in a country aiming for prosperous citizens. Efforts to improve housing and settlement environments, including slums, have been many but are less effective in eradicating slum housing. To address this issue, the MICMAC Analysis method and descriptive analysis method are used to identify variables that play a role in consolidating slum land in urban areas. The study found two highly influential variables: Community Willingness to Accept Land Consolidation Programs (KMKL) and Funding Sources (SPBY), and two variables with low influence and dependency: Administration and Documentation in Land Consolidation Implementation (ADPKL) and Benefits of Land Consolidation Implementation (KMPKL). A key variable for the next 20 years is the Social & Economic Conditions of the Community (KSEM). The planned land consolidation in Ciumbuleuit focuses on infrastructure improvement and procurement, and further slum housing arrangements must consider land area, community willingness, budget, and other variables. This study is expected to provide insights for planning and implementing land consolidation and could offer new ideas to improve the success of land consolidation planning for slum settlements in urban areas. Abstrak. Pemenuhan kebutuhan akan lahan menjadi salah satu langkah untuk mencapai kesejahteraan sosial bagi seluruh rakyat Indonesia yang merupakan bagian dari tanggung jawab pemerintah. Perbedaan antara masyarakat yang kebutuhan perumahannya terpenuhi dan masyarakat yang tinggal di daerah kumuh menjelaskan adanya kesenjangan sosial yang seharusnya tidak boleh terjadi sebagai sebuah negara yang bertujuan untuk menjadi rakyat yang sejahtera. Upaya yang dilakukan dalam perbaikan lingkungan perumahan dan pemukiman yang termasuk kumuh sudah banyak dilakukan, namun kurang efektif dalam pemberantasan perumahan kumuh. Untuk memecahkan masalah tersebut maka digunakan metode Analisis MICMAC dan metode analisis deskriptif untuk mengetahui variabel yang berperan dalam konsolidasi lahan perumahan kumuh di kawasan perkotaan. Hasil dari penelitian ini adalah terdapat 2 variabel yang paling berpengaruh, diantaranya Kesediaan Masyarakat Menerima Program Konsolidasi Lahan (KMKL) dan Sumber Pembiayaan (SPBY), dan 2 variabel yang memiliki pengaruh dan ketergantungan yang rendah, yaitu Administrasi dan Dokumentasi Dalam Pelaksanaan Konsolidasi Lahan (ADPKL) dan Keuntungan/Manfaat Pelaksanaan Konsolidasi Lahan (KMPKL), variabel yang berpengaruh untuk 20 tahun kedepan, yaitu Kondisi Sosial & Ekonomi Masyarakat (KSEM). Konsolidasi lahan yang direncanakan di Kelurahan Ciumbuleuit difokuskan untuk pembenahan dan pengadaan infrastruktur, begitu pula untuk penataan perumahan kumuh lebih lanjut harus mempertimbangkan luas lahan, kesediaan masyarakat, anggaran dan variabel lainnya. Diharapkan kajian ini dapat menjadi bahan refleksi perencanaan dan pelaksanaan konsolidasi lahan dan dapat menjadi pokok pemikiran baru untuk meningkatkan keberhasilan perencanaan konsolidasi lahan untuk penangan permukiman kumuh di kawasan perkotaan.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.025
GPT teacher head0.242
Teacher spread0.217 · 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 teacher head, not a consensus.

Study designNot applicable
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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