Analisis Faktor-Faktor Yang Mempengaruhi Perilaku Wajib Pajak Terhadap Penggunaan E-Filing
Bibliographic record
Abstract
The purpose of this research is to analyze the influence of perceived usefulness, perceived ease of use, security and privacy to the use of e-Filing. The object of this research is the individual taxpayer who uses e-Filing and registered in the Tax Office (KPP) Pratama Kosambi.. This research used primary data in the form of questionnaires were 117 pieces. The method used in this research is the causal study and the sampling technique that used is convenience sampling. The method that used is multiple regression analysis. The results of this study indicate that: (1) perceived usefulness has influence on the use of e-Filing, (2) perceived ease of use has influence on the use of e-Filing, (3) the security and privac has influence on the use of e-Filing, (4) perceived usefulness, perceived ease of use, security and privacy have influence simultaneously on the use of e-Filing. Keywords: perceived usefulness, perceived ease of use, security and privacy, the use of Filing
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".