Sistem Pemantauan Faktor Daya Listrik Rumah Tangga Berbasis IoT
Bibliographic record
Abstract
Perkembangan teknologi dan IT yang cepat akhir- akhir ini membuat masyarakat semakin erat berhubungan dengan perangkat pintar, big data, cloud, dan IoT. Fenomena ini menyebar lebih cepat berdasarkan infrastruktur komunikasi kabel dan nirkabel yang disediakan untuk pemantauan dan pengaturan peralatan listrik kebanyakan tempat tinggal sebagai perangkat hubung komunikasi ke beberapa perangkat terminal dengan beragam fungsi. Dalam studi ini, sistem pemantauan dalam perbaikan faktor daya pada listrik rumah tangga telah dibuat terintegrasi dengan perangkat IoT sehingga setiap setiap data parameter listrik dan status dari perbaikan faktor daya dapat diakses datanya secara realtime melalui aplikasi web yang bisa diakses dimana pun menggunakan perangkat elektronik seperti smartphone, notebook, laptop dan juga komputer.Dari hasil pengujian menunjukkan bahwa data faktor daya beserta data parameter listrik lainnya dapat diakses datanya secara realtime database dengan aplikasi web dan bisa diakses dimanapun menggunakan perangkat seperti android, notebook, laptop dan juga komputer.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.011 |
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".