ANALISIS EFEKTIFITAS PENDAPATAN PAJAK RESTORAN DIKECAMATAN LABUHAN RATU BANDAR LAMPUNG
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui seberapa besar kontribusi pajak restoran dan sejauh mana efektivitas pajak restoran Kecamatan Labuhan Ratu di Kota Bandar Lampung. Penelitian ini dilakukan di Kecamatan Labuhan Ratu tepatnya pada restoran yang ada di Kecamatan Labuhan Ratu dengan menggunakan data BPPRD Kota Bandar Lampung. Hasil penelitian menunjukkan bahwa rata-rata kontribusi pajak restoran terhadap Pendapatan Asli Daerah Kota Bandar Lampung periode 2019-2022 sebesar 0,25% - 0,28% per tahun. Angka ini memperlihatkan bahwa kontribusi pajak restoran terhadap Pendapatan Asli Daerah masih sangat kurang baik dan tingkat efektivitas pengelolaan pemungutan pajak restoran Kota Bandar Lampung selama periode tahun 2019-2022 dapat dikatakan efektif, dengan rata-rata tingkat efektifitas 84,03%. Semakin tinggi rasio efektifitasnya, menggambarkan pemungutan pajak restoran cukup baik. Kata Kunci: Pendapatan Asli Daerah, dan Pajak Restoran. This research aims to find out how big the contribution of restaurant taxes is and the extent of the effectiveness of restaurant taxes in Labuhan Ratu District in Bandar Lampung City. This research was conducted in Labuhan Ratu District, specifically at a restaurant in Labuhan Ratu District, using BPPRD data from Bandar Lampung City. The research results show that the average contribution of restaurant taxes to Bandar Lampung City Regional Original Income for the 2019- 2022 period is 0.25% - 0.28% per year. This figure shows that the contribution of restaurant taxes to Original Regional Income is still very poor and the level of effectiveness in managing restaurant tax collection in Bandar Lampung City during the 2019-2022 period can be said to be effective, with an average level of effectiveness of 84.03%. The higher the effectiveness ratio, the better the restaurant tax collection. Keywords: Regional Original Income, and Restaurant Tax.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".