EKSTRAKSI CITRA PADA PROSES KEAMANAN KRIPTOGRAFI MEMANFAATKAN ALGORITMA SECURE HASH (SHA)
Bibliographic record
Abstract
The development of increasingly sophisticated information and communication technology is inseparable from the growing importance of long distance communication. Current information exchange is not only in the form of text, but can also be in the form of images. All that can be done using an internet connection. With this internet connection, you can connect with many people. Cryptography aims to prevent images from being seen by unauthorized persons so that information stored on a computer is safe or sent through an internet connection. And can protect the confidentiality of the image from various threats that arise. SHA-256 is one of the hashing functions that is safe enough to be used to transform a byte of data into a hash string. The system built has been able to implement the SHA 256 algorithm which can change the original image file in the form of unknown files and encrypted images that cannot be known, unless read using an application that has been built. the image when encrypted and reused when decrypted with as many as 45 characters and may be in the form of numbers or letters. The SHA 256 process when encrypted is only one cycle, can be played with decryption.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.029 |
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".