FAKTOR-FAKTOR YAG MEMPENGARUHI KEPATUHAN WAJIB PAJAK DALAM MEMBAYAR PAJAK BUMI DAN BANGUNAN PERDESAAN DAN PERKOTAAN (STUDI KASUS PADA WAJIB PAJAKN PBB-P2 KENAGARIAN KOTO TINGGI KECAMATAN BASO KABUPATEN AGAM)
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui Faktor-Faktor Yang Mempengaruhi Kepatuhan Wajib Pajak Dalam Membayar Pajak Bumi Dan Bangunan Perdesaan Dan Perkotaan (Studi Kasus Pada Wajib Pajak PBB-P2 Kenagarian Koto Tinggi Kecamatan Baso Kabupaten Agam). Penelitian ini berlokasikan di Jorong Batu Taba, Kecamatan Baso, Kabupaten Agam. Berdasarkan pada kriteria sampel yang telah ditentukan sebelumnya, maka pengambilan sampel pada penelitian ini menggunakan metode purposive sampling dengan metode pengumpulan data berupa penyebaran kuesioner yang disebarkan kepada 151 responden Jorong Batu Taba Kecamatan Baso, Kabupaten Agam. Analisis data dalam penelitian ini menggunakan analisis deskriptif, analisis regresi linear berganda, uji asumsi klasik dan uji hipotesis. Hasil penelitian ini menunjukkan bahwa SPPT, pengetahuan wajib pajak, kualitas pelayanan pajak, kesadaran wajib pajak, pendapatan wajib pajak dan sanksi perpajakan secara serentak berpengaruh positif dan signifikan terhadap kepatuhan wajib pajak. Sedangkan secara parsial SPPT, kualitas pelayanan pajak, kesadaran wajib pajak, pendapatan wajib pajak dan sanksi perpajakan berpengaruh positif dan signifikan terhadap kepatuhan wajib pajak. Kata Kunci : SPPT, pengetahuan wajib pajak, kualitas pelayanan pajak, kesadaran wajib pajak, pendapatan wajib pajak, sanksi perpajakan, kepatuhan wajib pajak
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".