Design and Construction of Monitoring and Control System in Swallow House Based on IoT
Bibliographic record
Abstract
This study aims to design and build an Internet of Things (IoT) based monitoring and control system in a swiftlet building. Swiftlet nest farming is a rapidly growing industry in Indonesia, but still faces challenges in terms of monitoring and optimal environmental management for swiftlet productivity. The system developed integrates sensors to measure critical environmental parameters such as temperature. The research methodology includes hardware design using a microcontroller, sensors, and actuators connected to an IoT network. Software is developed to process sensor data, send it to blynk, and present information through a web interface that can be accessed remotely. This system is also equipped with an automatic control feature to keep environmental parameters within the optimal range. The results of the study show that the system is capable of real-time monitoring and providing notifications when an anomaly occurs. The automatic control feature successfully maintains temperature stability in the swiftlet building, where when the temperature read by the sensor is less than or equal to 25oC the lights will turn on and if the temperature is more than 29oC the lights will turn off. In conclusion, the implementation of this IoT-based monitoring and control system provides an effective solution to increase efficiency and productivity in swiftlet nest farming. This research opens up opportunities for further development in the application of IoT technology in the business sector, especially in swallow farming.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".