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Record W4378881848 · doi:10.56799/jceki.v1i6.1021

Implementasi Pencegahan Pelanggaran Tata Ruang Dengan Fungsi SKRK dan IMB di Kota Surabaya

2022· article· id· W4378881848 on OpenAlexaff
Linda Puspita Sari, Fx. Valentino David Adiso Pandiangan, Bambang Arwanto

Bibliographic record

VenueJ-CEKI Jurnal Cendekia Ilmiah · 2022
Typearticle
Languageid
FieldEngineering
TopicUrban Transport Systems Analysis
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsHumanitiesPolitical scienceForestryGeographyPhilosophy

Abstract

fetched live from OpenAlex

Memberikan SKRK (Surat Keterangan Rencana Kota) dan IMB (Izin Mendirikan Bangunan) kepada pemilik lahan yang akan melakukan kegiatan pembuatan konstruksi bangunan, maka pemilik lahan sebagai pemohon izin sudah terikat secara hukum. Untuk mentaati semua undang-undang dan aturan-aturan yang tertulis dalam SKRK dan IMB serta surat-surat perizinan lain yang diperlukan berkaitan dengan penggunaan konstruksi gedung yang dibangun, pelanggaran terhadap SKRK, IMB, dan izin kelengkapan yang lain akan menimbulkan sanksi hukum kepada pemilik lahan/persil. Sanksi bisa berupa pembongkaran sebagian konstruksi bangunan sampai sanksi administrasi. Hasil penelitian menunjukkan bahwa tindakan hukum terhadap pelanggaran aturan yang ada SKRK dan IMB serta kelengkapan izin yang lain perlu/harus dilakukan. Penegakan hukum dilakukan untuk menjaga kepentingan masyarakat banyak, menjaga kelestarian alam, menjaga lingkungan hidup maka perlu diciptakan aturan untuk penanggulangannya atau paling tidak bisa meminimalisir permasalahan bencana banjir, kemacetan lalu lintas,dan pencemaran lingkungan hidup. Yang mana aturan-aturan itu juga harus didukung oleh masyarakatnya sendiri.

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)0.004
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.053
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0530.010

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 head0.015
GPT teacher head0.221
Teacher spread0.207 · 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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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