Pendeteksian Kebocoran pada Jaringan Pipa Berbasis Internet of Things (IoT) dengan Notifikasi dan Lokalisasi Sumber Kebocoran
Bibliographic record
Abstract
This study aims to design and develop a pipeline leakage detection system based on the Internet of Things (IoT) that provides real-time notifications and determines the location of leaks with high accuracy. Pipeline leakage is a serious issue, as it can lead to water wastage, environmental damage, and high maintenance costs. Therefore, a system that can detect leaks quickly and accurately is crucial for improving the efficiency of pipeline infrastructure management. The system developed in this study uses an ESP32 microcontroller, Waterflow sensor, and GPS module. The ESP32 microcontroller serves as the central processing unit that processes the data received from the Waterflow sensor and the GPS module. The Waterflow sensor detects changes in water flow that indicate a leak in the pipeline. When an abnormal reduction in flow is detected, the sensor sends a signal to the microcontroller. The GPS module then provides location coordinates, pinpointing the exact location of the leak, allowing the maintenance team to quickly address the issue. Additionally, the system is integrated with the Blynk application, which enables remote monitoring through a mobile device. The Blynk application provides a user interface that facilitates the monitoring of pipeline status and delivers notifications whenever a leak is detected. Testing results show that the IoT-based leakage detection system is capable of identifying leaks and sending real-time information with good accuracy. With this system, the process of identifying and addressing pipeline leaks can be done faster and more efficiently, ultimately reducing the losses caused by leakage. The system also offers a more effective solution for pipeline maintenance, improving the reliability of water distribution systems and reducing water resource wastage.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".