ANALISA KINERJA BUS RAPID TRANSIT (BRT) TRANS SEMARANG KORIDOR II TERMINAL TERBOYO-TERMINAL SISEMUT
Bibliographic record
Abstract
Semarang as one of the big cities in Central Java has provided public transportation which is Bus Rapid Transit (BRT) as an effort to reduce congestion and the use of private transpotation. There are eight main corridor and one special corridor that are provides until 2021, one of them is Corridor II with Terboyo-Sisemut Route. This study is aim to analyze the servce performance of Corridor II with the optimalization the use of BRT in this route, find the problem factors that influence and formulate the step for quality services improvement. The method of this study is quantitative method by calculating the weight value through assessment indicators based on the standards of the Director General of Transportation. These indicators are obtained from the results of dynamic surveys and static surveys. From the analysis, the service performance of BRT Corridor II at Terminal Terboyo-Sisemut PP is in good category. The number of fleets needed in corridor II is 21 units. Based on the results of the evaluation, one recommendation to improve the quality service of BRT is to make a special lane for BRT to make travel time faster, so that users are more interested in using BRT.
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 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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".