TINDAK PIDANA DUNIA MAYA BERUPA VIRUS DAN TROJAN HORSE MENURUT UNDANG-UNDANG NOMOR 11 TAHUN 2008 TENTANG INFORMASI DAN TRANSAKSI ELEKTRONIK
Bibliographic record
Abstract
Cybercrime or crime at cyberspace has many forms or shapes, but from that all existings forms, hacking is a forms that gets a lot of attention at the UN Congress X in Vienna st hacking is The first crime, also seen from The technical aspects, hacking have excess. First, The man who hacking must be can do other forms of cybercrime with ability to enter into computer system and then broke that system. Second, technically the quality of the hacking result from hacking that more seriously if compared with other forms of cybercrime, such as viruses and The Trojan Horse. Computer media and cyber world becomes most targets that attack by hackers because regarded as media that common owned by all levels of society. As that becomes problem in this research is how an arrangement crime of Virus and The Trojan Horse, and how the law enforcements tackling crime of Virus and The Trojan Horse. Research approach used normative juridical, the collected data both primary and secondary data examine by juridical review with not eliminate other nonjuridical element. This approach leads to laws and regulations as a major study of law and behavior of the perpetrator that wrongly use technology and information as concrete support to strengthening that juridical analysis. Result of research indicated that the role of law enforcement in handling crimes of Viruses and Trojan Horse that exercised so far was still very minimal.This cause many obstacles found by law enforcements, the existing statuary barriers, constraints of investigation, and the resistance of the people themselves.The most important thing is the system verification in order to cope with the crime of Viruses and Trojan Horse through repair or revision of existing statuary barriers, whether Law No.11 Year 2008 and The other regulation that related with The crimes of Viruses and The Trojan Horse.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.012 |
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".