Rancang Bangun Sistem Monitoring Emisi Gas Buang Pada Ruang Parkir Bawah Tanah Gedung Perkantoran Menggunakan Internet of Things (IoT)
Bibliographic record
Abstract
Penelitian ini bertujuan merancang dan membangun sistem monitoring emisi gas buang di ruang parkir bawah tanah gedung perkantoran dengan memanfaatkan teknologi Internet of Things (IoT). Sistem ini mengintegrasikan sensor MQ-7 dan MQ-135 untuk mendeteksi gas berbahaya seperti karbon monoksida (CO), sulfur dioksida (SO₂), dan nitrogen oksida (NOₓ). Data hasil deteksi dikirim secara real-time melalui modul ESP32 ke aplikasi Blynk, sehingga memungkinkan pemantauan kualitas udara secara terus-menerus dan jarak jauh. Selain itu, sistem ini juga dilengkapi dengan indikator visual berupa LED dan buzzer sebagai peringatan dini apabila konsentrasi gas melebihi ambang batas yang ditentukan. Hasil pengujian menunjukkan bahwa sistem ini mampu mendeteksi dan memantau emisi gas secara akurat serta memberikan notifikasi yang dapat digunakan sebagai dasar pengambilan tindakan preventif. Dengan demikian, sistem ini dinilai efektif dan andal dalam menjaga kualitas udara di area parkir tertutup. Kesimpulannya, implementasi sistem monitoring berbasis IoT ini berpotensi besar untuk meningkatkan keselamatan dan kesehatan pengguna ruang parkir bawah tanah melalui pemantauan kualitas udara yang efisien, real-time, dan responsif terhadap kondisi lingkungan.
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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) | 0.001 | 0.001 |
| 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) | 0.011 | 0.004 |
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".