Analisis Kepercayaan Pengguna E-Money (Studi Pada Masyarakat di Wilayah Bandung Raya)
Bibliographic record
Abstract
The financial payment system has changed along with technological developments. One of them is electronic money or e-money. The benefits of e-money are considered important, especially during the Covid-19 pandemic where it is recommended to reduce physical contact in suppressing the spread of the Covid-19 virus. The level of use of e-money continues to increase, and even so, consumer trust, one of which is data security and e-money finance is still a matter of concern. This study aims to determine the trust of e-money users in the Greater Bandung area. Consumer trust is measured through four dimensions, namely benevolence, ability, integrity, and willingness to depend. The quantitative descriptive method is the research method used in this study by distributing questionnaires to 220 respondents. The results of this study suggest that the trust of e-money users in the Greater Bandung area is high.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".