Jaringan Saraf Tiruan (JST) Memprediksi Penyakit Rubella Menggunakan Metode Backpropagation
Bibliographic record
Abstract
The development of Information Technology is now entering various sectors, including health, and is implemented in Bidadari Binjai Hospital. As a health institution that is committed to excellent service and quality, Bidadari Binjai Hospital needs to innovate technology. One health issue that requires attention is rubella, an airborne infectious disease that has the potential to cause serious disorders such as hearing loss, cataracts, speech delay, and heart failure in toddlers and children. The initial symptoms of rubella are often similar to other common diseases, so public understanding of these symptoms is very important for quick treatment. This research aims to develop an information technology-based system that is able to predict rubella using the backpropagation method. This method is expected to improve the accuracy of diagnosis and make it easier for people to recognize rubella symptoms early on. The proposed system aims to provide better diagnosis support at Bidadari Binjai Hospital, as well as increase public awareness and knowledge about rubella disease. From the research conducted, the results of the accuracy rate obtained when conducting a test program were selected according to the symptoms and the results obtained were rubella disease with a 100% accuracy rate.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".