An implentation of IoT for environmental monitoring and its analysis using k-NN algorithm

Prayitno, Eko; Fahmi, Nurul; Rasyid, M. Udin Harun Al; Sudarsono, Amang

doi:10.12928/telkomnika.v19i6.15724

Cited by 2 publications

(3 citation statements)

References 18 publications

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“…Based on findings [6] k-NN method accuracy rate was 99.0657% for observed temperature and humidity datasets. In the paper [8] authors employed a machine learning approach, specifically the random forest method, to develop a prediction model for estimating relative humidity.…”

Section: Rationale For Deciding To Use Knn Svm and Rf With Real Hardw...mentioning

confidence: 87%

“…The k-nearest neighbours (k-NN) [6], [7], [8] classification algorithm utilizes the Euclidean distance metric between two points to assess the proximity of unknown samples to those with known classes. Subsequently, the unknown sample is associated with the most prevalent class within its set of k-nearest neighbours.…”

Section: Rationale For Deciding To Use Knn Svm and Rf With Real Hardw...mentioning

confidence: 99%

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IoT-Based Machine Learning System for Physical Intrusion Detection Using Dynamic Temperature and Humidity Observations With Raspberry Pi

Hajdarevic

2024

TEM Journal

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This paper presents a system that is able to detect physical intrusion in a specific space based on temperature and humidity change. This specific space was housing hardware components important for information security management infrastructure. Presented system is able to predict that two spaces are connected and that there is a physical breach in protected space. The presented prediction approach involves identifying patterns in historical data, where the subsequent outcomes are already known in advance, and validating these patterns using more recent data. System is implemented using k-Nearest Neighbours, Random Forest, and Support Vector Machine algorithms in Python programming language on Raspberry Pi. Real observed data to predict if specific temperature and humidity indicates intrusion were used. This approach can be used to detect intrusions in the room or in other closed space. More specifically thermal equilibrium phenomenon between two spaces after barrier between them are opened was monitored. Through process of supervised learning using labelled data, system was able to detect intrusion by using k-nearest neighbours, random forest, and support vector machine with different accuracy. Presented model shows better results using k-nearest neighbours and support vector machine with accuracy of 100% compared to random forest with accuracy of 95%. The system is low cost because of cheap Raspberry Pi controller and sensors.

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Section: Rationale For Deciding To Use Knn Svm and Rf With Real Hardw...mentioning

confidence: 87%

Section: Rationale For Deciding To Use Knn Svm and Rf With Real Hardw...mentioning

confidence: 99%

IoT-Based Machine Learning System for Physical Intrusion Detection Using Dynamic Temperature and Humidity Observations With Raspberry Pi

Hajdarevic

2024

TEM Journal

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“…There are two main ways for computer sensors to gather data: in-situ and remote. In-situ means that it uses sensors that touch the object of interest to gather data, for example, IoT system for environment monitoring system [10] [11]. Remote, as in remote sensing, means the system uses sensors that don't directly interact with the object of interest [12].…”

Section: Introductionmentioning

confidence: 99%

Mangrove Tree Density Detector using YOLO Based on Darknet Framework using RGB Drone Imagery

Yudhistira Kurniawan,

Udin Harun Al Rasyid,

Sukaridhoto

2024

IJCDS

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Mangrove preservation is crucial due to their ecological significance impact. Monitoring the health of mangrove forests is essential for preservation strategy, yet it remains challenging and time-intensive, particularly in remote locations. This study aims to create system to automatically assess mangrove density, providing essential data for informed preservation strategies, such as prioritizing reforestation in low density area. Using drones with RGB cameras to capture aerial imagery, enabling remote data collection. The system utilizes the YOLO neural network object detector to automatically detect objects, enabling quantity estimation. Experiment shows that YOLO object detector is able to detect mangrove tree accurately with 95% recall, 88.3% IoU, and 22ms processing time. The system uses 'tiny' model variant to provide more efficient accuracy compared to computation resource, making it suitable for deployment on computer with limited resources. In comparison to standard model that improves the recall by 4%, IoU by 2%, but demands six times more processing time. Then it calculate the covered area using camera transformation formula. Finally, it calculates the density for mangrove forest health, synchronized with GPS location. With the resulting data on mangrove density, evaluations of mangrove forest health become much easier, facilitating effective preservation actions, such as reforestation in area with low mangrove density.

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An implentation of IoT for environmental monitoring and its analysis using k-NN algorithm

Cited by 2 publications

References 18 publications

IoT-Based Machine Learning System for Physical Intrusion Detection Using Dynamic Temperature and Humidity Observations With Raspberry Pi

IoT-Based Machine Learning System for Physical Intrusion Detection Using Dynamic Temperature and Humidity Observations With Raspberry Pi

Mangrove Tree Density Detector using YOLO Based on Darknet Framework using RGB Drone Imagery

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