ANALISIS HYBRID CRYPTOSYSTEM ALGORITMA ALGORITMA RSA DAN TRIPLE DES
Bibliographic record
Abstract
Keamanan data sangat dibutuhkan dalam hal berkomunikasi. Untuk menjamin keamananan data dibutuhkan teknik untuk menyadikan data dan informasi yang disebut dengan Kritografi. Penelitian ini bertujuan untuk mengalisis proses Hybrid Didalam Kritografi simetris dan Asimetris yang mengunakan Algoritma RSA dan Algoritma Triple DES. Hal ini dapat meningkatkan keamanan data sehingga data menjadi lebih terjaga kerahasiaannya. Metode yang digunakan Algoritma RSA (Riverst – Shamir- Adleman) ini merupakan algoritma asimetris menggunakan sistem bilangan prima secara Acak dalam proses keamanannya dan Algoritma Triple DES yang disebut jugan dengan algoritma simetris adalah metode OFB (Output feeback), dan sehingga ketika kedua algoritma ini digabungkan dalam proses Hybrid maka keamanan datanya semakin akurat. Analisis Hybrid Kriptosistem algoritma RSA dan algoritma Triple DES menunjukan bahwa data yang dibuat secara text akan dienkripsi menjadi chipertext dengan menggunakan kedua metode tersebut dan di deskripsikan kembali. Sehingga keamanan data nya relative aman.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".