Assessing Indonesian MSMEs' Awareness of Personal Data Protection by PDP Law and ISO/IEC 27001:2013
Bibliographic record
Abstract
Digital technology, while streamlining business operations, also poses significant risks by recording vast amounts of data.This study evaluates the awareness and compliance of Indonesian MSMEs with the Personal Data Protection (PDP) Law and ISO/IEC 27001:2013 standards, highlighting areas needing improvement.Using a quantitative approach, an online questionnaire was distributed to 126 MSMEs across Indonesia to assess legal awareness, consent management, data processing, and governance structures.The analysis, employing descriptive statistics and a Likert scale, reveals a low awareness of the PDP Law (mean score: 3.13) and partial compliance in consent management (mean score: 3.49).While data processing shows strengths (mean score: 3.71), weaknesses in third-party agreements (mean score: 2.67) and the appointment of Data Protection Officers (mean score: 2.98) indicate governance gaps.The findings underscore the struggle of Indonesian MSMEs in implementing crucial data protection practices.The study recommends investing in legal and data protection training, formalizing data agreements, appointing Data Protection Officers, conducting regular audits, and improving data breach management.These steps are vital for fostering a data protection culture and ensuring business sustainability in the digital age.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".