Studi Jalur Pedestrian Kawasan Pendidikan Kota Sukabumi Berbasis UNA
Bibliographic record
Abstract
Abstract. A pedestrian-friendly city is an indicator of a livable city. However, the education area in Cikole Sub-district, as the center of Sukabumi City, is not fully supported by an optimal pedestrian network. This contradicts the direction of spatial policy set out in the Sukabumi City RTRW 2022-2042. This study used Urban Network Analysis (UNA) approach with ArcGIS software to evaluate the connectivity and efficiency of road network through Reachness and Straightness Index indicators. The analysis aims to identify the level of accessibility of educational facilities to the pedestrian network and how network interventions, such as the addition of new roads, can affect movement patterns. The results of this study will form the basis for formulating a pedestrian path development concept that supports connectivity between the education area and surrounding settlements. The purpose of this study is to develop a proposal to optimize the pedestrian network in the education function area in Sukabumi City Center in order to facilitate pedestrian movement that meets the standards, especially for education function areas from one place to another effectively and efficiently and safely for pedestrians. Abstrak. Kota yang ramah terhadap pejalan kaki merupakan indikator kota yang layak huni. Namun, kawasan pendidikan di Kecamatan Cikole, sebagai Pusat Kota Sukabumi, belum sepenuhnya didukung oleh jaringan pedestrian yang optimal. Hal ini bertentangan dengan arah kebijakan tata ruang yang tertuang dalam RTRW Kota Sukabumi 2022–2042. Penelitian ini menggunakan pendekatan Urban Network Analysis (UNA) dengan alat bantu perangkat lunak ArcGIS untuk mengevaluasi konektivitas dan efisiensi jaringan jalan melalui indikator Reachness dan Straightness Index. Analisis ini bertujuan untuk mengidentifikasi tingkat aksesibilitas sarana pendidikan terhadap jaringan pejalan kaki dan bagaimana intervensi jaringan, seperti penambahan jalan baru, dapat memengaruhi pola pergerakan. Hasil dari penelitian ini akan menjadi dasar dalam merumuskan konsep pengembangan jalur pedestrian yang mendukung konektivitas antara kawasan pendidikan dan permukiman sekitar. Tujuan penelitian ini adalah menyusun usulan untuk mengoptimalkan jaringan jalur pedestrian di kawasan dengan fungsi pendidikan pada Pusat Kota Sukabumi agar dapat memfasilitasi pergerakan pejalan kaki yang sesuai standar khususnya untuk kawasan dengan fungsi pendidikan dari satu tempat ke tempat lain dengan efektif dan efisien serta aman bagi pejalan kaki.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".