PENGARUH PERSEPSI KEBERMANFAATAN, PERSEPSI KEMUDAHAN,PERSEPSI KERUMITAN, DAN PERSEPSI KEPUASAN WAJIB PAJAKTERHADAP PENGGUNAAN E-FILLING BAGI WAJIB PAJAK ORANG PRIBADI DI KOTA KUDUS
Bibliographic record
Abstract
Pajak merupakan sumber pendapatan negara yang sangat penting. Berbagai macam terobosan terbaru terkait dengan perkembangan teknologi dan informasi, maka Direktorat Jendral Pajak memperkenalkan Electronic System Filing atau yang lebih dikenal dengan sistem e-filing. Penerapan sistem e-filing akan sangat membantu wajib pajak dalam melaporkan pajaknya, namun dalam hal ini beberapa pesepsi wajib pajak yang berbeda beda tentang sistem ini maka akan mempengaruhi penggunaan sistem tersebut. Penelitian ini bertujuan untuk mengetahui bagaimana pengaruh antara persepsi kebermanfaatan, persesi kemudahan, persepsi kerumitan dan persepsi kepuasan pengguna terhadap penggunaan e-filing khususnya untuk wajib pajak orang pribadi di wilayah Kota Kudus. Analisis data yang digunakan adalah analisis regresi berganda dengan bantuan program SPSS. Hasil penelitian menunjukkan bahwa persepsi kebermanfaatan, persepsi kemudahan, persepsi kepuasan pengguna berpengaruh terhadap penggunaan e-filing. Namun pada persepsi kerumitan tidak berpengaruh terhadap penggunaan e-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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".