ANALISIS FASILITAS PAJAK PERTAMBAHAN NILAI DIBEBASKAN (STUDI KASUS BIAYA JASA PENGELOLAAN SUMBER DAYA AIR)
Bibliographic record
Abstract
Deprivatisasi sektor layanan publik, termasuk industri pengelolaan air bersih, terjadi di berbagai negara. Pengelolaan air bersih di Indonesia dilakukan oleh Perusahaan Daerah yang tergabung dalam Persatuan Perusahaan Air Minum Seluruh Indonesia (Perpamsi). Salah satu masalah yang mengganjal bagi Perpamsi dalam penyediaan air bersih bagi publik adalah tingginya beban pajak, yaitu Biaya Jasa Pengelolaan Sumber Daya Air (BJPSDA), Pajak Air Permukaan dan Pajak Pertambahan Nilai (PPN). Bahkan terdapat pengenaan PPN atas BJPSDA, sehingga mereka mengusulkan diberikannya fasilitas PPN dibebaskan atas jenis jasa ini. Penelitian ini bertujuan untuk melakukan analisis pemberian fasilitas PPN dibebaskan atas BJPSDA. Penelitian dilaksanakan dengan metode kualitatif yang bersifat explanatory study. Metode penelitian menggunakan prinsip pengenaan PPN berdasarkan Ottawa Framework sebagaimana direkomendasikan oleh Organisation for Economic Cooperation and Development (OECD). Pemberian fasilitas PPN harus memenuhi prinsip umum : netralitas, efisiensi, efektifitas dan keadilan, kepastian dan, kesederhanaan serta fleksibilitas. Hasil penelitian membuktikan bahwa kebijakan PPN dibebaskan atas BJPSDA berpotensi melanggar prinsip netralitas, efisiensi, efektifitas dan kepastian, dan kesederhanaan. Sementara itu prinsip kepastian dan keadilan, serta fleksibilitas dapat terpenuhi.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".