IMPLEMENTASI SUPER ENKRIPSI ALGORITMA ONE TIME PAD (OTP) DAN BEAUFORT CHIPER UNTUK MENGAMANKAN DATA
Bibliographic record
Abstract
Perkembangan teknologi informasi yang semakin pesat memberi pengaruh yang besar hamper diserluruh aspek kehidupan manusia. Tentunya tingkat keamanan yang tinggi sangat diperlukan agar informasi tersebut tidak dapt diakses oleh orang yang tidak berkepentingan. Masalah pengiriman pesan atau data merupakan salah satu hal yan sangat penting, karena data sangat rentang terhadap tindakan kejahatan komputer. Kriptografi super enkripsi dapat menjadi salah satu solusi untuk mencegah tindakan kejahatan. Algoritma One Time Pad dan Beaufort Chiper merupakan salah satu algoritma yang dapat digunakan untuk menjaga kerahasiaan data, One Time Pad adalah algoritma yang menggunakan perhitungan XoR dalam enkripsi dan dekripsi sedangkan Beaufort Chiper adalah algoritma yang menggunakan perhitungan modulo. Jika kedua algoritma ini disatukan dalam sebuah aplikasi keamanan data, maka akan sulit untuk dibobol atau diambil oleh orang yan tidak bertanggung jawab.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".