IMPLEMENTATION OF ELECTRONIC-BASED GOVERNMENT SYSTEM IN JAKARTA: THE CASE OF CYBER ATTACKS
Bibliographic record
Abstract
Information security in the Electronic-Based Government System (SPBE) of the DKI Jakarta Provincial Government is not yet optimal. The study found that 682 applications are vulnerable to cyber attacks, including the JAKI application in 2023. The causal factors include inadequate security budgets, insufficient personnel with BSSN certification (only 14 out of 31), and infrastructure that does not meet the ISO 27001: 2022 standards. Additionally, Information Security Standard Operating Procedures (SOPs) are not yet available in every Regional Apparatus Organization (OPD), and support from the Regional Council (DPRD) is still minimal. This study aims to analyze the deficiencies in SPBE information security in DKI Jakarta and provide recommendations for improvement. The theoretical foundations used are Government Science, Information Security, and Public Policy Implementation. The research method employs a qualitative approach, using secondary data analysis from official reports and literature reviews on public policy implementation and information security standards. The study concludes that increased budgets, personnel training and certification, as well as infrastructure and SOP improvements are urgently needed. As a global city, Jakarta must improve its information security to protect its data and reputation, thereby enhancing the overall information security index. The recommendations include increasing the information security budget to 6-10% of the total SPBE budget, providing training and certification for all relevant personnel, updating the infrastructure to meet the ISO 27001:2022 standard, and developing and implementing Information Security SOPs in each OPD . Strong policy support from the DPRD is also crucial to strengthen these efforts and support Jakarta's position as a Global City.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".