Perancangan Pengenalan Karakter Alfabet menggunakan Metode Hybrid Jaringan Syaraf Tiruan
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
Self Organizing Maps adalah salah satu metode dalam jaringan syaraf tiruan yang menggunakan pembelajaran tanpa supervisi yang digunakan untuk meng-cluster neuron-neuron berdasarkan kelompok tertentu. Backpropagation adalah salah satu metode dalam jaringan syaraf tiruan yang menggunakan pembelajaran dengan supervisi yang populer dan memiliki keunggulan dalam kemampuan pembelajarannya. Algoritma hybrid dari self organizing maps dan backpropagation dapat meningkatkan performansi dan akurasi dari pembelajaran, terutama dalam pengenalan karakter alfabet. Hal ini dibuktikan dalam proses hybrid dimana self organizing maps terlebih dahulu melakukan pembelajaran dan menghasilkan cluster dari karakter-karakter alfabet yang ada. Hasil cluster akan dijadikan sebagai input dalam pembelajaran backpropagation untuk mengenal karakter alfabet.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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