IDENTIFIKASI HAMA PADA TANAMAN KEDELAI DENGAN MENGGUNAKAN METODE FUZZY
Bibliographic record
Abstract
Kedelai adalah komoditas pangan utama di Indonesia selain padi dan jagung. Permintaan akan kedelai semakin meningkat karena kedelai mampu menjadi alternatif bagi masyarakat yang berminat pada makanan berprotein nabati rendah kolestrol. Namun bila dilihat dari hasil produksinya masih belum memuaskan. Hal ini disebabkan oleh berbagai faktor, salah satunya gangguan hama dan penyakit. Dalam mengidentifikasi hama, petani mengalami kesulitan. Gejala-gejala serangan yang terlihat juga memperlihatkan kesamaan bahkan gejala antara hama dengan penyakit yang hampir sama. Morfologi yang sama dari beberapa jenis hama yang berbeda juga mempengaruhi proses identifikasi. Metode yang digunakan adalah fuzzy, hal ini dilakukan karena parameter-parameter yang digunakan dalam penelitian ini (morfologi hama, gejala dan tingkat kerusakan) adalah variable kualitatif yaitu variable yang menunjukkan suatu intensitas yang sulit diukur memiliki sifat ambiguitas (tidak crips). Hasilnya metode fuzzy dapat mengidintifikasi hama pada tanaman kedelai dengan tingkat akurasi 77.78 %.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".