ANALISIS STRATEGI PENINGKATAN PENDAPATAN PAJAK KENDARAAN BERMOTOR PADA KANTOR UPT PENDAPATAN WILAYAH TANA TORAJA
Bibliographic record
Abstract
Tujuan penelitin ini adalah untuk mengetahui dan menganalisis faktor-faktor apa yang mempengaruhi kepatuhan wajib pajak kendaraan bermotor pada Kantor UPT Pendapatan Wilayah Tana Toraja dan mengetahui dan menganalisis bagaimana strategi pemerintah untuk meningkatkan pendapatan pajak kendaraan bermotor pada Kantor UPT Pendapatan Wilayah Tana Toraja. Jenis penelitian yang digunakan adalah kuantitatif. Menggunakan analisis SWOT. Hasil penelitian menunjukkan bahwa: 1) Faktor-faktor yang mempengaruhi kepatuhan wajib pajak kendaraan bermotor adalah tingkat pengetahuan wajib pajak tentang perpajakan, rendahnya pemahaman wajib pajak mengenai pajak kendaraan bermotor, dan kurangnya sosialisasi kepada wajib pajak mengenai pajak kendaraan bermotor. 2) Strategi pemerintah untuk meningkatkan pendapatan pajak kendaraan bermotor yaitu mengoptimalkan strategi intensifikasi dan ekstensifikasi, melakukan kerja sama dengan instansi lain kemudian meningkatkan sosialisasi peningkatan pendapatan pajak kendaraan bermotor kepada wajib pajak, sosialisasi dilakukan agar masyarakat lebih memahami tentang persyaratan pembayaran pajak kendaraan bermotor dan juga meningkatkan kesadaran wajib pajak akan pentingnya membayar pajak kendaraan bermotor dan juga mewujudkan program pembayaran pajak kendaraan bermotor melalui pelaksanaan program layanan terpadu bagi masyarakat sehingga melalui pelayanan tersebut diharapkan mampu menjangkau daerah terpencil sehingga masyarakat akan lebih mudah dalam membayar pajak kendaraan bermotor. The objectives of this research are: 1) To determine and analyze what factors influence the compliance of motor vehicle taxpayers at the Regional Revenue UPT Office of Tana Toraja. 2) To determine and analyze how the government strategy to increase motor vehicle tax revenue at the Regional Revenue UPT Office of Tana Toraja. The type of research used is quantitative. Using SWOT analysis. The results of this study show that: 1) Factors affecting motor vehicle taxpayer compliance are the level of taxpayer knowledge about taxation, the low understanding of taxpayers regarding motor vehicle taxes, and the lack of socialization to taxpayers regarding motor vehicle taxes. 2) The government's strategy to increase motor vehicle tax revenue is to optimize intensification and extensification strategies, collaborate with other agencies and then increase the socialization of increasing motor vehicle tax revenue to taxpayers, socialization is carried out so that people understand more about the requirements for paying motor vehicle taxes and also increase taxpayer awareness of the importance of paying motor vehicle taxes and also realize motor vehicle tax payment programs through the implementation of integrated service programs for the community so that through these services it is expected to be able to reach remote areas so that people will find it easier to pay motor vehicle taxes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 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".