Sistem Pakar Diagnosa Penyakit Kaligata Menggunakan Metode Dempster Shafer
Bibliographic record
Abstract
Kaligata merupakan bentol-bentol di kulit disertai ruam kemerahan, terasa gatal, dan terkadang terasa perih menyengat.Biasanya kaligata muncul akibat reaksi alergi. Perkembangan dunia teknologi informasi telah banyak mengalami perubahan yang sangat pesat, seiring dengan kebutuhan manusia akan teknologi informasi. Oleh karena itu sangat diperlukan suatu sistem yang bisa menjadi informasi dan media pengganti pakar dalam mendiagnosa penyakit kaligata, Agar pasien yang memiliki gejala sebelumnya dapat lebih cepat mendapatkan informasi dan konsultasi melalui sistem yang sudah dibuat. Sistem ini dibuat dengan Bahasa pemrograman PHP dan database MYSQL. Hasil dari peneltian ini adalah untuk merancang dan membangun sistem diagnosa penyakit kaligat dengan menggunakan metode dempster shafer dan untuk mempermudah pasien dalam berkonsultasi tanpa harus datang kepakar. Berdasarkan gejala tersebut yang telah dihitung untuk penyakit Kaligata , nilai densitas yang paling kuat adalah Kaligata Fisik yaitu sebesar 0,844.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.044 |
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".