ANALISIS SPASIAL KINERJA JALAN DAN SIMPANG DI KABUPATEN KUDUS
Bibliographic record
Abstract
One of the keys to smooth traffic flow in an area is characterized by optimal road and intersection performance. Planning or evaluating roads and intersections certainly requires existing data information, so storing data digitally will make subsequent handling activities easier. As technology is easy to use, it is very helpful in storing and presenting data. Geographic Information Systems present images, check, integrate, manipulate, analyze and display data that relates to the topographic conditions of the earth. The aim of this research is to conduct a spatial analysis of road and intersection performance in the CBD of Kudus Regency using ArcGIS. The data in this research includes primary data and secondary data. Primary data includes data on traffic volume, speed, capacity, V/C ratio, degree of saturation, segment length and service level. Meanwhile, secondary data is road network data. After data analysis, road and intersection performance data were synchronized with road network data using ArcGIS. The results of this research provide an information system related to road and intersection performance, making it easier to handle roads and intersections in the CBD Area of Kudus Regency. ABSTRAK Salah satu kunci lancarnya arus lalu lintas di sebuah wilayah ditandai dengan kinerja jalan dan simpang yang optimal. Perencanaan ataupun evaluasi jalan serta simpang tentu membutuhkan informasi data eksisting, sehingga penyimpanan data secara digital akan mempermudah kegiatan penanganan selanjutnya. Seiring mudahnya penggunaan teknologi, sangat membantu dalam penyimpanan dan penyajian data. Sistem Informasi Geografis menyajikan gambar, mengecek, mengintegrasikan, memanipulasi, menganalisan dan menampilkan data yang menghubungkan kepada kondisi topografi bumi. Tujuan dari penelitian ini adalah melakukan analisis spasial kinerja jalan dan simpang di CBD Kabupaten Kudus menggunakan ArcGIS. Data pada penelitian ini meliputi data primer dan data sekunder. Data Primer antara lain data volume lalu lintas, kecepatan, kapasitas, V/C rasio, derajat kejenuhan, panjang segmen dan tingkat pelayanan. Sedangkan data sekunder yaitu data jaringan jalan. Setelah dilakukan analisis data, kemudian data kinerja jalan dan simpang disinkrongkan dengan data jaringan jalan menggunakan ArcGIS. Hasil dari penelitian ini menyajikan sistem informasi terkait kinerja jalan dan simpang, sehingga mempermudah dalam penanganan jalan dan simpang di Kawasan CBD Kabupaten Kudus.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".