Sistem Kendali Penyiram Tanaman Menggunakan Propeller Berbasis Internet Of Things
Bibliographic record
Abstract
Penyiraman tanaman yang masih manual menjadikan tanaman tidak terawat dengan baik karena waktu aktifitas yang padat, atau jenis tanaman yang dimiliki memiliki perhatian khusus baik secara tempat yang harus sejuk dan kebutuhan air yang harus tetap terpenuhi.Jika penyiraman tanaman ini bisa dilakukan secara otomatis oleh bantuan alat maka akan sangat bermanfaat dan lebih mempermudah dalam proses perawatan tanaman. Penelitian ini bertujuan untuk merancang sebuah alat penyiram tanaman otomatis dengan menggunakan propellerdan sensor moinsture sebagai alat untuk mendeteksi kadar kelembaban tanah. Data diperoleh melalui (1) Penelitian Lapangan (2) Penelitian Pustaka (3) Wawancara. Hasil penelitian ini menunjukkan bahwa prototype penyiram tanaman menggunakan propeller berbasis internet of things dapat mempermudah dan menghemat waktu.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".