Implementation of the Lex Certa Principle towards the Ambiguity of Digital Law's in Indonesia
Bibliographic record
Abstract
Ambiguity in Indonesia's digital laws, particularly in the Electronic Information and Transactions Law (ITE’s Law), has raised concerns over the criminalization of free expression and the erosion of digital rights. Vague terms like "contents against propriety" and "inflicting hatred or dissension" in Articles 27 and Article 28 often lead to subjective interpretations, undermining legal certainty and exposing individuals to unfair prosecution. This study examines the role of the lex certa principle in addressing ambiguities within ITE’s Law and its application to safeguard digital rights. Employing a normative legal research method, that analyzes and systematizes legal norms, synthesizes doctrines, and provides prescriptive justifications aligned with existing laws to guide legal decision-making. This study examines relevant provisions, judicial precedents, and international best practices, including regulatory frameworks in Canada, the European Union, and the United States. The findings highlight that Indonesia's digital laws lack clarity, making them prone to misuse and inconsistent enforcement. Key cases, such as Prita Mulyasari and Baiq Nuril, illustrate the detrimental impact of ambiguous regulations on individuals' rights. The study proposes reforming ITE’s Law by redefining ambiguous terms, enhancing law enforcement training, and adopting proportionality tests to ensure restrictions on expression are justified and lawful. By incorporating lex certa into digital regulations, Indonesia can balance public order and freedom of expression while protecting digital rights. The study concludes with recommendations for multi-stakeholder collaboration in regulatory reforms to create a fair and inclusive digital legal framework.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".