IMPLEMENTASI MIKROKONTROLER ATMEGA328 DI BIDANG PERTANIAN DAN INDUSTRI
Bibliographic record
Abstract
Melihat perkembangan teknologi komunikasi, elektronikadan informatika saat ini yang begitu maju, telah mendorong pemikiran untuk memanfaatkan sumber daya tersebut untuk memenuhi kebutuhan manusia. Terdorongnya pemikiran untuk melakukan inovasi dan mengembangkan teknologi yang ada, telah banyak menstimulasi dikembangkannya pemanfaatan teknologi berdasarkan ilmu pengetahuan yang pada akhirnya memudahkan manusia menyelesaikan berbagai persoalan. Riset yang pertama secara umum membahas tentang bagaimana merancang sistem irigasi pintar berbasis mikrokontroler sebagai pengembangan dari sistem irigasi manual, bagaimana membangun protokol (software) layanan pada sistem irigasi pintar berbasis mikrokontroler agar aktivitas petani lebih maksimal dan bagaimana membangun layanan kontrol berbasis client-server untuk saluran irigasi primer-sekunder-tersier, sehingga terbangun sistem irigasi pintar terpadu. Sedangkan pada riset kedua, secara umum membahas tentang bagaimana merancang sensor getar berbasis mikrokontroler, bagaimana mengkalibrasi sensor getar berbasis mikrokontroleragar dapat dimanfaatkan untuk monitoring getaranrealtime mesin bubut horizontal serta bagaimana menentukan nilai ideal sensor getar terhadap getaran mesin bubut horizontal.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.017 |
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".