EFEKTIVITAS RESTORATIVE JUSTICE DALAM PENYELESAIAN KASUS KRIMINALITAS RINGAN: STUDI KASUS DENGAN METODE STUDI KOMPARATIF
Bibliographic record
Abstract
Restorative justice has gained attention as an alternative approach to resolving minor criminal cases, offering a rehabilitative and community-oriented process. In Indonesia, its implementation aims to reduce the judicial burden and promote offender reintegration. However, inconsistencies in application and its effectiveness in reducing recidivism remain concerns. This study evaluates the effectiveness of restorative justice in Indonesia by examining case resolution speed, victim satisfaction, and recidivism rates. Using a comparative study method with qualitative and quantitative approaches, data were collected from legal documents, case studies (2018–2023), and stakeholder interviews. The findings show that cases resolved through restorative justice increased from 500 in 2018 to 2,300 in 2023, reflecting broader adoption. Survey results indicate 70% of victims were satisfied with restorative justice outcomes, compared to 50% in conventional proceedings. The recidivism rate for offenders undergoing restorative justice was 40%, lower than 55% in conventional sentencing but still higher than the Netherlands (65%) and Canada (68%). This study highlights the importance of law enforcement support, victim participation, and community involvement in the success of restorative justice. Strengthening national policies is essential to ensure more consistent and effective implementation. These findings offer valuable insights for policymakers in enhancing restorative justice practices in Indonesia to build a more efficient and rehabilitative criminal justice system.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".