PERLINDUNGAN HUKUM TERHADAP DATA DAN INFORMASI PRIBADI DALAM SISTEM MANAJEMEN INFORMASI BAZNAS (SiMBA)
Bibliographic record
Abstract
Dalam perlindungan hukum terhadap data dan informasi pribadi dalam sistem manajemen informasi BAZNAS sangatlah penting. Sebagai pengguna kemanfaatan teknologi digital, perlu memahami bagaimana keamanan terhadap data dan informasi yang disimpan dalam media digital agar terhindar dari kebocoran data. Sebagai salah satu bagian dari teknologi, SiMBA perlu memperhatikan regulasi dan sistem keamanan agar data yang tersimpan tidak diretas oleh pihak ilegal. Penelitian ini menggunakan metode pendekatan yuridis normative. Penelitian ini menggunakan sumber data primer dan data sekunder. Narasumber dalam penelitian ini adalah Penanggung jawab SiMBA yang mengelola data dan informasi muzaki di Badan Amil Zakat Nasional (BAZNAS) Kota Bandar Lampung. Penelitian ini menganalisis data secara kualitatif, yakni menganalisis data primer dan sekunder guna menarik hasil kesimpulan. Hasil dari penelitian ini yaitu dalam pelaksanaan perlindungan terhadap data dan informasi pribadi di Badan Amil Zakat Nasional (BAZNAS) Kota Bandar Lampung, yang pertama penerapan di Badan Amil Zakat Nasional (BAZNAS) Kota Bandar Lampung, telah menjalankan perlindungan keamanan data tersebut dengan hanya mengizinkan pihak internal untuk mengakses data tersebut. Yang kedua bahwa Baznas kota Bandar Lampung sebagai user mengikuti ketentuan Undang-undang dalam menjaga data dan informasi secara teknis. \nKata Kunci : Perlindungan, SiMBA \n \nThe legal protection of personal data and information in the BAZNAS information management system is very important. As a user of the benefits of digital technology, it is necessary to understand how the security of data and information stored in digital media in order to avoid data leakage. As one part of technology, SiMBA needs to pay attention to regulations and security systems so that the stored data is not hacked by illegal parties. This research uses a normative juridical approach method. This research uses primary data sources and secondary data. The resource person in this research is the person in charge of SiMBA who manages muzaki data and information at the National Amil Zakat Agency (BAZNAS) of Bandar Lampung City. This research analyzes data qualitatively, namely analyzing primary and secondary data to draw conclusions. The results of this study are in the implementation of protection of personal data and information at the National Amil Zakat Agency (BAZNAS) of Bandar Lampung City, the first implementation at the National Amil Zakat Agency (BAZNAS) of Bandar Lampung City, has implemented the protection of data security by only allowing internal parties to access the data. The second is that Baznas Bandar Lampung city as a user follows the provisions of the Law in maintaining data and information securely. \nKeywords: Protection, SiMBA \n
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".