ANALISIS KEBIJAKAN IMPLEMENTASI BRT (BUS RAPID TRANSIT) PEMERINTAH KOTA MEDAN DALAM MENGATASI KEMACETAN LALU LINTAS DI KOTA MEDAN
Bibliographic record
Abstract
The purpose of this study is to analyze the implementation policy of BRT (Bus Rapid Transit) of Medan City Government in overcoming traffic congestion in Medan City. This type of research is qualitative. Primary and secondary data were used as the data sources. The primary data were based on interview results. Secondary data uses figures and data, books and scientific journals which are used as the basis for scientific literature. The data analysis technique uses thematic analysis. The BRT policy in Medan City has great potential to overcome traffic congestion and improve public transportation systems. However, the success of the implementation is highly dependent on infrastructure improvements, increased coordination between agencies, and public awareness of the importance of switching to more efficient and environmentally friendly public transportation. In the analysis of the driving factors for the success of the role of actors in the implementation of the Bus Rapid Transit (BRT) policy in Medan City in overcoming traffic congestion in Medan City, the driving factors for the success of the role of the Medan City Government consist of very wide accessibility, qualified resources consisting of Financial Resources, Human Resources and Partnership Resources, and having a communicative coordination system
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".