Implications of Legal Positivism on Cybercrime Law Enforcement in Indonesia in the Case of the Hacking of the Mojokerto City Government Website
Bibliographic record
Abstract
The existence of legal positivism that prioritizes legal certainty is expected to resolve various cybercrime criminal cases. One of them happened in the case of the Mojokerto City Government site hacking. The case has troubled the community, especially the city government of Mojokerto. The research method uses normative juridical research with a legal approach, concepts and collects primary legal materials in hacking rules and information technology in cybercrime crime. The study results found that the existence of legal positivism in the crime of cybercrime positions the law as statutory regulation and law as an order containing sanctions. In-Law No. 19 of 2016 concerning amendments to Law No. 11 of 2008 concerning Electronic Information and Transactions contains orders and prohibited actions. The actions taken by the suspect are deemed unfair if there are no sanctions or punishments because, according to legal positivism, justice that is legality (statutory regulation) is justice that prioritizes legal certainty. The law in force in article 46 paragraph 3 of Law No. 19 of 2016 concerning Amendments to Law No. 11 of 2008 concerning Electronic Information and Transactions must be imposed on the suspect in the Mojokerto City government website hacking case.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.051 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".