PENERAPAN ALGORITMA FIXED LENGTH BINARY ENCODING (FLBE) KOMPRESI CITRA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Perkembangan teknologi yang pesat, sangat berperan penting dalam pertukaran informasi yang cepat. Pada pengiriman informasi dalam bentuk citra masih mengalami kendala, diantaranya adalah karena besarnya ukuran citra sehingga solusi untuk masalah tersebut adalah dengan melakukan kompresi. Kompresi bertujuan untuk mengurangi ukuran data tersebut menjadi sekecil mungkin. Ada banyak metode kompresi citra, namun pada tugas akhir ini akan dibahas prinsip kerja algoritma Fixed Length Binary Encoding (FLBE) dengan implementasi menggunakan bahasa pemrograman visual basic. Analisis kinerja algoritma ini bertujuan untuk mengetahui performansi algoritma pada file citra. Untuk mengetahui hasil proses kompresi dilakukan melalui perhitungan Ratio of Compression (????????), Compression Ratio (????????), Redudancy (Rd), waktu kompresi (ms) dan waktu dekompresi (ms) pada file citra. Dalam percobaan yang dilakukan didapatkan bahwa algoritmaFixed Length Binary Encoding (FLBE) dengan rasio kompresi rata-rata sebesar 2.276%.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it