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Record W4312801896 · doi:10.25105/refor.v4i5.15133

PENDAFTARAN HAK TANGGUNGAN TERINTEGRASI SECARA ELEKTRONIK DI KANTOR PERTANAHAN KOTA DEPOK

2022· article· id· W4312801896 on OpenAlexaff
Elisabeth Putri, Dyah Setyorini

Bibliographic record

VenueReformasi Hukum Trisakti · 2022
Typearticle
Languageid
FieldSocial Sciences
TopicIndonesian Legal and Regulatory Studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHumanitiesPolitical sciencePhysicsArt

Abstract

fetched live from OpenAlex

Hak Tanggungan Terintegrasi secara Elektronik (HT-el) adalah serangkaian proses pelayanan hak tanggungan dalam rangka pemeliharaan data pendaftaran tanah yang diselenggarakan melalui sistem elektronik yang terintegrasi, sebagaimana diatur dalam Peraturan Menteri Agraria dan Tata Ruang/Kepala Badan Pertanahan Nasional Nomor 5 Tahun 2020 tentang Pelayanan Hak Tanggungan Terintegrasi secara Elektronik (Permen ATR/Ka.BPN Nomor 5 Tahun 2020). Pendaftaran Hak Tanggungan elektronik di Kantor Pertanahan Kota Depok masih dalam tahap pengembangan namun implementasi pendaftaran Hak Tanggungan elektronik telah sesuai Permen ATR/Ka.BPN Nomor 5 Tahun 2020, mulai dari persiapan sampai dengan terbitnya Sertipikat Hak Tanggungan Elektronik (Sertipikat HT-el), hal ini dipengaruhi diantaranya komunikasi, sumber daya, disposisi dan struktur birokrasi. Kendala yang timbul diantaranya berasal dari faktor internal misalnya kesiapan Kantor Pertanahan Kota Depok serta kesiapan pembenahan data; sedangkan yang berasal dari faktor eksternal misalnya kesiapan SDM yakni PPAT serta Bank selaku pemohon sehingga proses berlangsungnya Hak Tanggungan Elektronik menjadi terlambat yang akan berpengaruh pada lahirnya Hak Tanggungan. Kedepannya semua kendala wajib dibenahi, meningkatkan verifikasi data serta mengadakan pembinaan intensif pada seluruh pihak terkait.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0420.014

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 head0.258
Teacher spread0.243 · 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 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
Published2022
Admission routes1
Has abstractyes

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