Pengenalan Pola Pada Daun Sirih Menggunakan Metode Backpropagation
Bibliographic record
Abstract
Tanaman sirih memiliki berbagai jenis dengan bentuk dan warna yang mirip, yang sering kali menyebabkan kesulitan dalam mengidentifikasi dan membedakan jenis-jenisnya, terutama bagi masyarakat awam, lansia, dan penderita buta warna. Untuk mengatasi permasalahan ini, riset ini bertujuan untuk mengembangkan sistem identifikasi jenis daun sirih menggunakan metode Backpropagation pada jaringan saraf tiruan. Riset ini berfokus pada lima jenis daun sirih yang umum ditemui, yaitu sirih hijau, sirih merah, sirih hitam, sirih perak, dan sirih gading. Metode Backpropagation dipilih karena kemampuannya dalam mengenali pola yang kompleks melalui proses pembelajaran yang berulang. Sistem ini dikembangkan menggunakan bahasa pemrograman MATLAB, dengan dataset citra daun sirih yang diambil dengan resolusi tinggi. Proses identifikasi melibatkan beberapa tahap, termasuk akuisisi citra, praproses citra, ekstraksi fitur, dan klasifikasi menggunakan jaringan saraf tiruan
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".