Strengthening Courtroom Integrity in Indonesia: Addressing Technological and Security Challenges Through Global Best Practice
Bibliographic record
Abstract
Maintaining order in the courtroom is essential for ensuring fair and efficient judicial proceedings. However, Indonesian courts face increasing challenges in managing courtroom decorum due to evolving disruptive behaviors driven by technological advancements and societal changes under the 4th Industrial Revolution. This research examines the current state of courtroom management in Indonesia, identifying gaps such as unauthorized electronic recordings, inadequate systems for remote trial decorum, and insufficient courtroom security measures. Using a literature-methods approach, the study analyzes incident reports, judicial practices, and international best practices from countries like Germany, Japan, and Canada. Findings reveal significant weaknesses in the regulation of technology use, courtroom security frameworks, and the public perception of traditional practices. The research concludes that Indonesia's judiciary must adopt forward-thinking strategies to enhance courtroom management and public trust. Key recommendations for the Supreme Court include regulating electronic device usage, establishing a court police system, introducing summary procedures for in-court offenses, and revising the "all rise" practice to align with cultural sensitivities. These measures ensure a resilient, adaptive judiciary prepared to address current and future challenges effectively.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".