TELAAH KETENTUAN PIDANA KEKARANTINAAN KESEHATAN BERDASARKAN UNDANG-UNDANG NOMOR 6 TAHUN 2018 BAGI KESEHATAN NOTARIS DAN MASYARAKAT ERA PANDEMI COVID-19
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui kebijakan hukum pidana yang menghambat penerapan status darurat kesehatan masyarakat pada saat diselenggarakan kekarantinaan kesehatan dan menganalisis sistem pemidanaan yang ideal untuk diterapkan bagi pelaku tindak pidana kekarantinaan kesehatan. Metode penelitian yang digunakan adalah penelitian hukum normatif melalui pendekatan perundang-undangan (statute approach). Hasil penelitian menunjukkan bahwa secara teoretis kebijakan hukum pidana dalam darurat kesehatan masyarakat sulit untuk diterapkan. Substansi Pasal 93 UU Kekarantinaan Kesehatan memuat 2 (dua) jenis delik, yaitu delik formil dan delik materiel. Namun, terdapat penggunaan kata yang masih abstrak di antaranya: perbuatan “menghalang-halangi” serta menempatkan “kedaruratan kesehatan” sebagai “sebab” dalam peraturan tersebut merupakan sebuah kerancuan. Seharusnya rumusan kausalitas pidana dalam sebuah produk hukum pidana dirumuskan sesuai dengan konsepsi awalnya. Oleh karena itu, rumusan delik yang abstrak atau luas akan menghasilkan ketidakpastian hukum, berpotensi tidak dapat diterapkan, dan bertentangan dengan penafsiran yang menyatakan bahwa hukum pidana harus ditafsirkan secara sempit. Merujuk pada keadaan tersebut, maka sistem pemidanaan yang ideal diterapkan ketika terjadi pelanggaran penyelenggaraan kekarantinaan kesehatan, yaitu sistem pemidanaan yang bersifat restoratif dan integratif.Kata kunci: Pandemi Covid-19, Kekarantinaan Kesehatan, Kebijakan Hukum Pidana AbstractThis study aims to determine the legal policy policies that apply Law Number 6 of 2018 concerning Health Quarantine and analyze the ideal punishment system to be applied to health quarantine crimes. The research method used, namely normative research through an invited approach (statute approach). The results show that legal policies in public health emergencies are difficult to implement. The substance of Article 93 of the Health Quarantine Law contains 2 types of offenses, namely formal offenses and material offenses. However, there is a use of the word which is still abstract beside: the act of "obstructing" and placing "health emergency" as "cause" in the regulation is a confusion. The formulation of criminal causality in a criminal law product should be formulated in accordance with its initial conception. Therefore, the abstract or broad formulation of offenses will provide legal uncertainty, which cannot be applied, and contradicts the interpretation which states that criminal law must be interpreted narrowly. Referring to this situation, the ideal punishment system is applied when implementing health quarantine, namely a restorative and integrative system of punishment.Keywords: Covid-19 Pandemic, Health Quarantine Act, Penal Policy
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.069 | 0.013 |
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".