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Record W7131215793 · doi:10.14710/jpk.13.1.83-91

PENGUKURAN DAN PENINGKATAN TINGKAT PELAYANAN JALUR PEJALAN KAKI DI JALAN BRAGA KOTA BANDUNG

2025· article· W7131215793 on OpenAlexaff
Kezia Rianka Putri Aprianto, Martina Cecilia Adriana, Herika Muhamad Taki

Bibliographic record

VenueJurnal Pengembangan Kota · 2025
Typearticle
Language
FieldEngineering
TopicUrban Transport Systems Analysis
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsGreen beltCross-border cooperationSubbase

Abstract

fetched live from OpenAlex

Ketersediaan jalur pejalan kaki yang aman dan nyaman salah satu dasar penting dalam keberlanjutan perkotaan. Jalan Braga merupakan kawasan cagar budaya dan pariwisata dengan aktivitas utama perdagangan dan jasa. Pada hari biasa, persepsi terhadap jalur pejalan kaki di Braga tergolong baik dari segi fasilitas. Akan tetapi, di hari libur, jumlah pejalan kaki menjadi sangat padat yang menyebabkan terganggunya sirkulasi dan kenyamanan berjalan. Penelitian ini bertujuan untuk mengukur tingkat pelayanan jalur pejalan kaki Braga saat padat serta merumuskan upaya peningkatan pelayanan yang paling sesuai. Metode yang digunakan dalam penelitian ini adalah deskriptif kuantitatif dengan teknik analisis mengacu pada Highway Capacity Manual (HCM) 2022. Temuan penelitian menunjukkan bahwa tingkat pelayanan jalur pejalan kaki Braga saat ramai mencapai tingkat terendah yaitu tingkat F. Dengan menguji berbagai skenario, hanya pedestrianisasi yang mampu meningkatkan tingkat pelayanan menjadi D yaitu kondisi berjalan yang nyaman dengan kecepatan normal.

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)0.002
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 score0.059
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0590.015

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 head0.009
GPT teacher head0.224
Teacher spread0.215 · 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
Published2025
Admission routes1
Has abstractyes

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