Prediksi Disleksia pada Anak menggunakan Metode Naive Bayes
Bibliographic record
Abstract
Dyslexia is a neurological disorder that affects a person's ability to read, spell, and understand words with a level of difficulty that is not in accordance with their level of intelligence or education. It is a lifelong condition that can affect the way the brain processes information related to reading and written language skills. This study uses the Naïve Bayes method. is a method that uses probability and statistical calculations. And the advantage of the Naïve Bayes classification is that this method only requires a small amount of training data to determine the parameter estimates needed in the classification process. The purpose of this study is to find out how to solve the diagnosis or problems arising from dyslexia in children made in an expert system using the Naïve Bayes method and to find out the results of making the system can replace an expert into a computer so that the diagnosis is easier and faster. The results of this study are by selecting the symptoms that occur in children according to what is experienced, the system can find out the child or patient can be diagnosed quickly according to consultation with a dyslexia expert.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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.
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".