Studi Eksploratif Penanganan Faktur Pajak yang Tidak Berdasarkan Transaksi yang Sebenarnya
Bibliographic record
Abstract
Upaya penanganan Faktur Pajak yang Tidak Berdasarkan Transaksi yang Sebenarnya (FP TBTS) atau faktur pajak fiktif oleh Direktorat Jenderal Pajak (DJP) melalui pembentukan Satuan Tugas Penanganan FP TBTS pada tahun 2014 dan 2015 belum dapat mencegah terjadinya kembali kasus tersebut pada tahun 2016. Melalui penelitian ini, peneliti bertujuan untuk menelaah penyebab berulangnya kasus tersebut dan strategi penanganan idealnya. Dengan menggunakan pendekatan studi kasus dalam metode kualitatif, peneliti memfokuskan unit analisis pada Direktorat Penegakan Hukum dan Direktorat Intelijen Perpajakan. Peneliti melakukan pengumpulan data melalui wawancara mendalam dan melakukan analisis dengan model Miles and Huberman. Adapun hasil penelitian yang didapat terdiri dari gambaran umum kasus dan penanganan, penyebab kasus berulang, dan strategi penanganan ideal. Berdasarkan hasil penelitian tersebut, peneliti mendapatkan simpulan bahwa penyebab berulangnya kasus adalah kesempatan pelaku seperti pihak penerbit faktur pajak fiktif dan pihak perantara, serta proses bisnis pada Kantor Pelayanan Pajak (KPP). Sebagai strategi penanganan ideal, diperlukan pembaharuan sistem informasi dan perubahan mekanisme PPN.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".