Analisis Perbandingan Akurasi dalam Identifikasi Autism dengan SVM dan Naive Bayes
Bibliographic record
Abstract
Gangguan autisme banyak ditemukan pada anak yang berumur 3 tahun ke bawah. Pendiagnosaan gangguan penyakit ini telah dilakukan dengan menggunakan berbagai metode, terutama metode dalam dunia psikologis. Peneliti menggunakan metode Support Vector Machine (SVM) dan metode Naive Bayes untuk menyelesaikan kasus gangguan autisme yang mengalami kesalahan diagnosa. Dalam hasil penelitian ini dilakukan perbandingan metode Support Vector Machine (SVM) dengan metode Naive Bayes. Metode Support Vector Machine (SVM) menghasilkan rata ?¢â?¬â?? rata klasifikasi 93,12%, sedangkan metode Naive Bayes menghasilkan rata ?¢â?¬â?? rata klasifikasi 73,34%.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.008 | 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".