Kandang Ayam Pintar Berbasis Internet of Thinks Menggunakan NodeMCU ESP8266
Bibliographic record
Abstract
Perancangan perangkat kandang ayam pintar berbasis IoT. Sistem perangkat ini menggunakan mikrokontroler NodeMCU ESP8266 yang berfungsi sebagai pengolah data dan pengirim perintah dan juga sebagai penerima jaringan WI-FI. Sistem perangkat kendang ayam pintar ini menggunakan system kontrol menggunakan smartphone android untuk mengontrol dan memonitoring keadaan, kendang ayam pintar ini menggunakan system komunikasi jaringan WI-FI sehingga sistem kendang ayam dan smartphone android dapat terhubung, dalam system kendang ayam pintar ini menggunakan sensor dht11 sebagai pengontrol suhu yang ada di dalam kendang ayam, dan motor servo sebagai penggerak penutup pakan ayam. Supply tegangan yang di gunakan oleh alat ini adalah AC-DC colokan listrik rumah untuk di hububgkan ke lampu, sumber tegangan yang di butuhkan pompa air 5volt. Tegangan AC-DC masuk terlebih dahulu ke rangkaian relay, 5 volt tengangan untuk menghidupkan pompa air. Alat ini dapat diguanakan dengan mudah untuk membantu kegiatan manusia dalam memelihara ayam hanya dengan menggunakan smartphone dan di hubungkan ke WI-FI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.076 | 0.034 |
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".