DAMPAK GUNA LAHAN TERHADAP TINGKAT KEMAMPUAN KINERJA JALAN STUDI KASUS : JALAN AHMAD YANI DI KECAMATAN KARTASURA
Bibliographic record
Abstract
The use of land as a trade and service area contributes greatly to the development of urban economic structures, including in the District of Kartasura. Especially Kartasura's market activity which is always developing because it is a place to fulfill primary needs. However, the existence of this market and also the trade and service activities around it are increasingly troubling due to irregularities and disrupting transportation activities around the market. Geographically, Kartasura Subdistrict is quite close to the Surakarta City area (around 10 Km), and Surakarta City has a very rapid and dense development intensity and has a limited development area, so the development of socio-economic activities tends to move towards the Kartasura Sub-District area.Close socio-economic relations with Surakarta City made Kartasura experience rapid development in the growth of new activities along the A. Yani road. Such as education, health, trade and services, industry and office activities. With the growth of new activities along the A Yani road, traffic jams often occur at peak hours. The congestion is due to the mixture between modes of transportation, trade, industry and offices.The purpose of this study is to identify the performance of the A Yani road, to determine the effect of land use on congestion that occurs. To achieve these objectives, the analysis used is quantitative calculations. By comparing the road conditions at peak and non peak hours on the A Yani road. The method used in this study is analyzing the volume of the road (V) A Yani experiencing congestion, analyzing side barriers, speed, road capacity (C) A Yani, and the level of road service (V / C) A Yani. In addition to the quantitative analysis also conduct qualitative analysis to clarify the quantitative analysis that has been done. So that what is a problem on Jalan A Yani can also be analyzed, what are the factors that cause congestion and finally show conclusions and recommendations of problems.Keywords: Land use, Traffic Congestion
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".