Victim Protection in Indonesian Criminal Justice: Assessing the Implementation of Restorative Justice as a New Paradigm
Bibliographic record
Abstract
The purpose of this study is to analyze the new paradigm in the protection of crime victims in Indonesian criminal law through the implementation of restorative justice, by identifying and comparing the laws of countries that have successfully implemented it, to determine the extent to which the principle has been accommodated in the national legal system. The research method used is normative legal writing with a statutory regulatory approach, legal concept analysis, and comparative law. The results indicate that the paradigm for protecting crime victims in Indonesian criminal law remains dominated by a retributive approach focused on perpetrators, while victims tend to be passive and lack comprehensive recovery. Although regulations such as the Criminal Procedure Code and the Law on Witness and Victim Protection exist, their implementation has not systematically integrated restorative justice principles. This study highlights the need for a new paradigm that places victims as the main subject through a restorative justice approach based on dialogue, recovery, and active participation. Through comparative analysis of practices in New Zealand, Canada, and the Netherlands, it is shown that restorative justice can be effectively implemented if supported by clear regulations, institutional support, and awareness among law enforcement officers. Therefore, the Indonesian criminal law system needs to be updated by formulating a contextual, just, and sustainable restorative justice-based victim protection model, to create a justice system more responsive to victims’ needs.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".