Passenger perception of commuter line service quality in Indonesia
Bibliographic record
Abstract
This research aimed to study the opinion and perspectives of Commuter Line passengers in Indonesia by using 18 attributes of service quality. There still needed to be more understanding about which service attributes were less satisfying and which were more pleasing to the Commuter Line passengers in the area of Jakarta and its surroundings. This research used factor analysis and Principal Component Analysis to select among the 18 Commuter Line service quality variables with the Varimax and Ordered Logit model rotation method. The number of samples used was 384 respondents from Commuter Line passengers in Jakarta and its surroundings. The result of factor analysis stated that the 18 attributes of service quality with three factors were the main attributes of service quality being used, namely the factor of station facilities and passenger behavior, the factor of ticket and security system, and they had reasonably strong correlations. The key finding of this research was that some service quality attributes, such as the crowd or density of trains, station stair facility, station lift facility, station seat facility, and shelter, were perceived as the attributes of service that were less satisfying. This research provided valuable insights into important factors affecting the opinion and perspective of Commuter Line passengers in Jakarta and its surroundings.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".