Tinjauan Yuridis Perlindungan Korban Terhadap Kejahatan Cyber Bullying Dalam Sistem Hukum Pidana Indonesia
Bibliographic record
Abstract
Undang- Undang Nomor 19 tahun 2016 tentang Informasi dan Transaksi Elektronik dan KUHP . meskipun ITE dapat digunakan untuk menindak pelaku cyberbullying dalam beberapa kasus, namun ketiadaan undang-undang yang khusus menjadi kendala dalam penanganan kasus, baik berupa upaya pembuktian, pemenuhan hak korban, dan kendala yang sering dihadapi dalam penangan kasus cyberbullying. Tipe penelitian yang akan digunakan dalam penelitian ini mengenai"Tinjauan Yuridis Perlindungan Korban Terhadap Kejahatan Cyber Bullying Dalam Sistem Hukum Pidana di Indonesia" adalah penelitian hukum yuridis normatif. Dengan adanya landasan hukum ini, pemerintah dapat mengembangkan pedoman dan aturan yang efektif untuk mencegah tindakan pelecehan online. Deterrence atau efek jera menjadi salah satu dampak positif dari perlindungan hukum. Ancaman sanksi hukum yang jelas dapat menjadi penghambat potensial bagi individu yang ingin melakukan cyber bullying. Dengan demikian, perlindungan hukum dapat berperan dalam menciptakan lingkungan online yang lebih aman. Perlindungan hukum memberikan hak kepada korban untuk melaporkan kasus cyber bullying dan mendapatkan keadilan. Hak-hak ini mencakup perlindungan fisik, hak privasi, dan hak mendapatkan kompensasi atas kerugian yang dialami. Sebagai bagian dari upaya perlindungan, proses hukum juga dapat memberikan perasaan keamanan kepada korban.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".