Pengenaan Biaya Tambahan Pada Transaksi Jual Beli Di E-commerce dan Marketplace
Bibliographic record
Abstract
In sales transactions through e-commerce platforms, this issue is examined from the perspective of consumer protection law in Indonesia. This issue is raised due to the absence of norms regarding price transparency and maximum limits on additional fees imposed by businesses, which has an impact on legal uncertainty for consumers. This study formulates two main problems, namely, the weakness of regulations on additional fees outside the base price in the Consumer Protection Law, and ideal alternative regulations for such practices in the context of e-commerce in Indonesia. The research method used is a legal normative approach combining legislative, conceptual, and comparative methodologies. The findings indicate that Indonesia’s legal framework has not yet specifically addressed the imposition of additional fees, despite such practices already occurring across various platforms. Article 10 of the UUPK on regulates base prices and discounts, without covering additional fees. Comparative studies with Canada and Australia show that both countries have regulated price transparency through regulations that require clarity of information and consistency between advertised prices and charged prices. Based on these findings, and ideal legal framework adapted from best practices in both countries is proposed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".