Shariah Assessment Toward the Prosecution of Cybercrime in Indonesia
Bibliographic record
Abstract
This research aims to uncover how Islamic criminal acts towards social media crimes. This study also elaborates on how Islam assesses Indonesian criminal prosecution against social media crimes. The approach used is a juridical normative to assess the criminal law system in force in Indonesia with the Islamic criminal perspective as grand theory. The results found that crime through social media was adapted with the crime in Islamic law namely Hudūd, qisas diyat and tazir. This research also found that the Indonesian legal system provides legal rewards for perpetrators of crimes through social media charged with the Information and Electronic Transactions (ITE) Law still needs to be expanded. Crimes through social media most often threatened by the ITE Law are insults to the government or symbols of the state, threatening and defamation of others, insults to others and violating SARA (ethnicity, religion, race and intergroup). Cybercrimes related to adultery, alcoholism and terrorism must be considered because they are a serious threat. Prison penalties and fines that are most often sentenced to perpetrators of social media crimes include part of criminal tazir which is following Islamic criminal law.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".