PENERAPAN ALGORITMA RIVEST SHAMIR ADLEMAN (RSA) UNTUK MENGAMANKAN DATABASE PROGRAM KELUARGA HARAPAN (PKH)
Bibliographic record
Abstract
Untuk menjaga keamanan digunakan teknik enkripsi agar kerahasiaan data terebut terjamin. Salah satu algoritma enkripsi yang sering digunakan adalah algoritma RSA (Rivest Shamir Adleman). Pada kesempatan ini penulis tertarik mengkaji tentang aplikasi pengamanan database sql server. Pada penelitian ini, algoritma RSA (Rivest Shamir Adleman) digunakan sebagai pelindung database PKH (Program Keluarga Harapan), Sistem akan membangkitkan kunci public dan kunci private. Untuk mengamankan database PKH dienkripsi dengan kunci public Seluruh data akan dienkripsi, Sedangkan kunci private akan melakukan dekripsi atau mengembalikan dalam keadaan asli dengan algoritma RSA. Penerapan algoritma kriptografi RSA menjadi solusi yang baik pada sistem pengamanan database sql server yang akan digunakan untuk mengamankan database PKH. Untuk menjamin kerahasiaan data-data PKH yang disimpan didalam database, dengan penggunaan algoritma RSA ke dalam sistem tersebut maka data yang disimpan di dalam database sehingga isi datanya tidak dapat dimengerti oleh pihak lain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.019 |
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".