Analisis Perbandingan Akurasi dalam Identifikasi Autism dengan SVM dan Naive Bayes
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
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%.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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