Analyzing Benefits of Online Train Ticket Reservation App Using Technology Acceptance Model
Bibliographic record
Abstract
The research addresses the challenges faced by PT KAI (Kereta Api Indonesia) Persero in adapting to the digital revolution, particularly in the context of online ticket purchasing and train service reservations.It focuses on the need for PT.KAI to improve efficiency and customer satisfaction in response to complaints about conventional ticket purchase methods.It will employs the Technology Acceptance Model (TAM) to understand user acceptance and satisfaction with the KAI Access application.The sample size is quantified with 150 valid questionnaires and analyzes relationships between perceived ease of use (PEU), perceived usefulness (PU), attitude toward using (ATU), and intention to use.The results indicate that PEU positively influences PU, ATU, and intention to use, while PU positively influences ATU and intention to use.The study highlights the importance of understanding consumer needs in the digital era, emphasizing the significance of the KAI Access application in meeting these needs.The findings are expected to contribute to PT. KAI's service development and inspire similar companies facing challenges in the evolving digital landscape.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".