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Record W2997651435

ENKRIPSI PESAN TEKS DENGAN ALGORITMA ONE TIME PAD XOR DAN STEGANOGRAFI PADA CITRA GAMBAR DENGAN LEAST SIGNIFICANT BIT

2018· article· id· W2997651435 on OpenAlexaff
Eka Hari Setyawan, Novriyenni Novriyenni, Siswan Syahputra

Bibliographic record

Venuenot available
Typearticle
Languageid
FieldComputer Science
TopicComputer Science and Engineering
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsArtHumanities
DOInot available

Abstract

fetched live from OpenAlex

Kriptografi merupakan ilmu dan seni untuk menjaga keamanan pesan ketika pesan dikirim dari suatu tempat ke tempat lain. Kriptografi digunakan agar kerahasiaan pesan tersebut dapat terjaga, sehingga tidak diketahui oleh orang lain. Kriptografi ini muncul atas dasar keamanan dari suatu bentuk informasi-informasi penting yang bersifat rahasia. Steganografi merupakan cara menyisipkan pesan ke dalam media tertentu misalnya gambar, audio, video, dan lainnya. Cara ini terbukti efektif untuk mengamankan pesan teks dengan cara disisipkan pada file gambar, dan pesan tersebut tidak akan diketahui oleh pengguna lain jika didalam gambar yang sudah disisipkan pesan terdapat suatu pesan rahasia. Dan dalam penelitian ini dilakukan proses enkripsi dengan algoritma One Time Pad (OTP) XOR untuk mengubah pesan teks menjadi chiper atau pesan acak dalam bentuk bilangan biner. Kemudian chiper tersebut disisipkan ke dalam file gambar melalui bit-bit paling tidak berarti dengan metode Least Significant Bit (LSB). Kata Kunci : Kriptograf, Steganografi, One Time Pad (OTP), XOR, Least Significant Bit (LSB).

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.003

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.

Opus teacher head0.020
GPT teacher head0.216
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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