ANALISIS DAN PERANCANGAN TEKNIK FORWARD CHAINING UNTUK DETEKSI PENYAKIT SAPI DINAS PERIKANAN DAN PETERNAKAN BATU BARA
Bibliographic record
Abstract
Abstract: This research focuses on the analysis and design of a cattle disease detection system using the Forward Chaining technique. This system aims to help farmers identify various diseases in cattle quickly and accurately, which can ultimately improve livestock health and livestock productivity. In developing this system, the Forward Chaining technique is used as the main inference method. This method was chosen because of its ability to produce conclusions based on available facts in stages, so it is very suitable for expert system applications that require repeated and complex decision-making processes. This research begins with a system requirements analysis that includes identification of common types of cattle diseases, associated symptoms, as well as the knowledge and rules required for the diagnosis process. Next, system design is carried out which includes creating a knowledge base, inference engine, and user interface. The result of this research is a prototype expert system for diagnosing cattle diseases which has been tested and shows satisfactory performance in detecting various cattle diseases based on the symptoms entered. With this system, it is hoped that farmers can more quickly take appropriate action against diseases that attack their livestock, so that they can minimize losses and increase the efficiency of livestock businesses.Keywords: analysis; expert system; forward chaining; cattle disease; website.Abstrak: Penelitian ini berfokus pada analisis dan perancangan sistem deteksi penyakit sapi menggunakan teknik Forward Chaining. Sistem ini bertujuan untuk membantu peternak dalam mengidentifikasi berbagai penyakit pada sapi secara cepat dan akurat, yang pada akhirnya dapat meningkatkan kesehatan ternak dan produktivitas peternakan. Dalam pengembangan sistem ini, teknik Forward Chaining digunakan sebagai metode inferensi utama. Metode ini dipilih karena kemampuannya dalam menghasilkan kesimpulan berdasarkan fakta-fakta yang tersedia secara bertahap, sehingga sangat cocok untuk aplikasi sistem pakar yang memerlukan proses pengambilan keputusan berulang dan kompleks. Penelitian ini dimulai dengan analisis kebutuhan sistem yang mencakup identifikasi jenis-jenis penyakit sapi yang umum, gejala-gejala yang terkait, serta pengetahuan dan aturan yang diperlukan untuk proses deteksi. Selanjutnya, dilakukan perancangan sistem yang mencakup pembuatan basis pengetahuan, mesin inferensi, dan antarmuka pengguna. Hasil dari penelitian ini adalah sebuah prototipe sistem pakar deteksi penyakit sapi yang telah diuji dan menunjukkan kinerja yang memuaskan dalam mendeteksi berbagai penyakit sapi berdasarkan gejala-gejala yang dimasukkan. Dengan sistem ini, diharapkan peternak dapat lebih cepat dalam mengambil tindakan yang tepat terhadap penyakit yang menyerang ternaknya, sehingga dapat meminimalisir kerugian dan meningkatkan efisiensi usaha peternakan.Kata kunci: analisis; sistem pakar; forward chaining; penyakit sapi; website
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".