Implementation of Advanced Encryption Standard (AES) Algorithm for Employee Data Security at Binjai Religious Court
Bibliographic record
Abstract
In the digital era, data security in government institutions has become crucial, including at the Binjai Religious Court, which manages employee data in digital document form. Sensitive employee information such as identity, work history, and financial records is highly vulnerable to leakage and unauthorized access. To address this issue, this research implements the Advanced Encryption Standard (AES-128) algorithm as a data protection solution. The research methodology includes problem identification, literature study, system requirements analysis, design, implementation, and testing. The system was developed as a web-based application using PHP programming language, the CodeIgniter framework, and a MySQL database. AES is applied to encrypt and decrypt files in .docx and .xlsx formats. The test results show that encrypted files cannot be accessed without the correct key, while decrypted files can be fully restored to their original form. The encryption and decryption processes also run in relatively short times, making the system efficient and stable. In conclusion, the implementation of AES-128 successfully enhances the security of employee data at the Binjai Religious Court and can serve as a reference for other institutions in developing cryptography-based data security systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".