Face Mask Detection using Haar Cascade Classifier Algorithm based on Internet of Things with Telegram Bot Notification

Rahmatulloh, Alam; Gunawan, Rohmat; Sulastri, Heni; Pratama, Ihsan Adi; Darmawan, Irfan

doi:10.1109/icadeis52521.2021.9702065

Cited by 7 publications

(5 citation statements)

References 7 publications

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“…Some of the tests carried out also showed the success of sending data. Further research was carried out by A. Rahmatulloh, R. Gunawan, H. Sulastri, I. Pratama, and I. Darmawan [9]. This study uses the haar cascade algorithm to detect the use of face mask.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For the YoloV3 and YoloV4 algorithms, our tests were carried out using the same dataset and test data. However, for the Resnet 50 and Haar Cascade algorithms, the dataset is only based on previous studies [6], [9]. Overall the results of the comparison of accuracy obtained from each algorithm are shown in Table 3.…”

Section: +199 847+16+10+199mentioning

confidence: 99%

“…the highest accuracy, but the dataset is quite large. At the same time, the haar cascade algorithm does not get accuracy because previous research[9] only states that this algorithm is perfect for 12-60 fps video.…”

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confidence: 95%

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Utilization of Telegram application As an Information Media Face Mask Detection Result

Rasyid

Suradi²,

Arifin³

et al. 2023

SISTEMASI

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To know the results of the face mask detection system, one must be near a computer. This problem makes it difficult to reprimand and provide face masks to violators. One of the ways to prevent the spread of the virus is to wear a mask. This study focuses on making a face mask detection system connected to a cellular device. This study aims to make obtaining information more effortless, and monitoring officers can find out from a smartphone. As a medium of communication, we use the telegram application. Smartphone users widely use this application compared to existing messaging media applications. This study uses the YoloV4 algorithm to detect face mask and JSON to send information to the telegram application. The test consists of two stages, the first stage is to determine the accuracy of the face mask detection system and the second stage is to determine the average time required until the information is sent. The two tests performed obtained 97.57% and 0.255 seconds, respectively. The test results show that the system created can solve the existing problems. The researcher can do further research by increasing the number of datasets to increase the accuracy of face mask detection.Keywords: Face Mask Detection, JSON, Telegram Application, YoloV4 Algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Section: +199 847+16+10+199mentioning

confidence: 99%

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Utilization of Telegram application As an Information Media Face Mask Detection Result

Rasyid

Suradi²,

Arifin³

et al. 2023

SISTEMASI

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“…Concurrently, the Internet of Things (IoT) has emerged as a rapidly advancing concept, revolutionizing connectivity and data exchange across various applications [5,6]. In the realm of water monitoring [7][8][9][10], IoT devices have demonstrated their utility in communication, information sharing, and the coordination of decisions through network-integrated data analysis [11].…”

Section: Introductionmentioning

confidence: 99%

IoT-Enabled Water Distribution Monitoring: A Sensor-Based Analytical Model

Rahmatulloh,

Supriatna,

Widiyasono

et al. 2023

ISI

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Water service providers are confronted with multifaceted challenges ranging from ensuring service quality to addressing technical issues such as pipeline leaks. Manifestations of such leaks are often detected through diminished water pressure, yet the monitoring systems employed by these providers remain suboptimal. Traditional methodologies for identifying and inspecting distribution issues rely heavily on manual, analog procedures. This study seeks to harness the Internet of Things (IoT) for the development of an enhanced water distribution monitoring system. Employing a quantitative experimental methodology, the proposed system integrates an Arduino Uno microcontroller with sensors for water pressure, flow, and GPS location. Data captured by these sensors is synchronized with the Thinger.io platform via an ESP-32 module, facilitating real-time monitoring. Upon evaluation, it was observed that IoT implementation via Thinger.io could effectively track fluctuations in water discharge and pressure. Recorded data revealed a maximum water discharge of 303 liters, with water pressure values ranging from a low of 0.30 bar to a high of 4.07 bar. The outcome of this research is a visual monitoring framework that provides a dynamic assessment of water discharge and pressure, enabling water service providers to conduct routine evaluations with enhanced precision. This IoT-based approach not only streamlines the monitoring process but also introduces a level of automation that could significantly improve service reliability for drinking water infrastructures.

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“…Internet of Things (IoT) is one of the new trends in the technology world. Simply put, IoT connects physical devices such as CCTV, lights, televisions, refrigerators, and even house doors to the Internet continuously and can be controlled remotely via a smartphone [1], monitor it [2], [3], or issue information to other devices [4], [5]. Recently, many reports of attacks on IoT device vulnerabilities have been reported [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

Identification of Mirai Botnet in IoT Environment through Denial-of-Service Attacks for Early Warning System

Rahmatulloh

Ramadhan

Darmawan

et al. 2022

JOIV : Int. J. Inform. Visualization

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The development of computing technology in increasing the accessibility and agility of daily activities currently uses the Internet of Things (IoT). Over time, the increasing number of IoT device users impacts access and delivery of valuable data. This is the primary goal of cybercriminals to operate malicious software. In addition to the positive impact of using technology, it is also a negative impact that creates new problems in security attacks and cybercrimes. One of the most dangerous cyberattacks in the IoT environment is the Mirai botnet malware. The malware turns the user's device into a botnet to carry out Distributed Denial of Service (DDoS) attacks on other devices, which is undoubtedly very dangerous. Therefore, this study proposes a k-nearest neighbor algorithm to classify Mirai malware-type DDOS attacks on IoT device environments. The malware classification process was carried out using rapid miner machine learning by conducting four experiments using SYN, ACK, UDP, and UDPlain attack types. The classification results from selecting five parameters with the highest activity when the device is attacked. In order for these five parameters to be a reference in the event of a malware attack starting in the IoT environment, the results of the classification have implications for further research. In the future, it can be used as a reference in making an early warning innovative system as an early warning in the event of a Mirai botnet attack.

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Face Mask Detection using Haar Cascade Classifier Algorithm based on Internet of Things with Telegram Bot Notification

Cited by 7 publications

References 7 publications

Utilization of Telegram application As an Information Media Face Mask Detection Result

Utilization of Telegram application As an Information Media Face Mask Detection Result

IoT-Enabled Water Distribution Monitoring: A Sensor-Based Analytical Model

Identification of Mirai Botnet in IoT Environment through Denial-of-Service Attacks for Early Warning System

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