Penerapan Sistem Pakar untuk Mendiagnosa Penyakit Pencernaan dengan Pengobatan Bahan Alami
Bibliographic record
Abstract
Penerapan sistem pakar untuk mendiagnosa penyakit pencernaan dengan pengobatan dari bahan alami menggunakan metode forward chaining. Metode forward chaining merupakan metode inferensi untuk penalaran dari suatu masalah dengan memberikan solusinya. Penelitian ini sebagai produk teknologi terapan yang diharapkan memberi manfaat sebagai media konsultasi atau instruktur bagi masyarakat pada umumnya, dan terkhusus bagi dokter dan paramedis pada klinik, puskesmas dan rumah sakit dalam memberikan alteratif pencegahan dan pengobatan secara alami. Perancangan sistem telah dilakukan melalui aktivitas pengumpulan data, perancangan rules, perancangan proses dan pengujian sistem. Tahun pertama menghasilkan produk aplikasi sistem pakar yang telah diimplementasikan pada sejumlah puskesmas dan pada tahun kedua dilakukan pengujian model integrasi sistem pakar terhadap berbagai gejala penyakit pencernaan yang terjadi dengan memberikan alternatif solusi pencegahan penyakit berdasarkan hasil inference yang ditemukan dengan pengobatan cara alami. Hasil pengujian sistem dinyatakan baik dengan tingkat akurasi 91,56%. Kata kunci: diagnosa, forward chaining, penyakit pencernaan, sistem pakar,
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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 | 0.002 | 0.003 |
| 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 | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.011 |
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".