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All Journal Journal of Computer Networks, Architecture and High Performance Computing Jurnal Info Sains : Informatika dan Sains
Effendi, M Makmun
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Computer Science & IT Education

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Design of Mask Detection Application Using Tensorflow Lite based on Android Mobile Effendi, M Makmun; Turmudi, Ahmad; Arwan, Asep
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4329

Abstract

A mask is a type of personal protective equipment (PPE) that is essential for protecting the nose and mouth from contamination by droplets or airborne particles. The use of masks became highly popular during the Covid-19 pandemic, which began in December 2019 in China and peaked in Indonesia in 2020. Despite the pandemic subsiding and vaccinations increasing immunity, some companies still require masks to prevent the spread of illnesses such as colds and flu, especially in work processes that produce smoke, such as soldering and welding. To ensure employees comply with mask usage, effective supervision is necessary. Manual supervision is less efficient, thus a digital detection method is needed. This study developed a mask detection application using deep learning algorithms and the TensorFlow Lite framework on an Android platform. The application can detect mask usage with 100% accuracy at a distance of 1 to 5 meters. The system was tested under various lighting conditions and environments to ensure reliability. Additionally, the implementation of this technology can be extended to other public areas to ensure compliance with health protocols. This tool helps companies easily monitor and enforce mask-wearing discipline among employees, thereby enhancing workplace safety and health. Future work could explore the integration of this system with other health monitoring tools to create a comprehensive safety solution.
Implementing Internet Of Things (IOT) Technology For Real-Time Detection And Monitoring Of LPG Gas Leaks Effendi, M Makmun; Zy, Ahmad Turmudi; Sanudin, Sanudin
Jurnal Info Sains : Informatika dan Sains Vol. 14 No. 03 (2024): Informatika dan Sains , Edition July - September 2024
Publisher : SEAN Institute

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Abstract

The Currently, LPG (Liquefied Petroleum Gas) is a vital resource for many households in Indonesia, as highlighted by the government's initiative to convert from kerosene to gas as a cooking fuel. The widespread adoption of LPG is attributed to its affordability and efficiency. However, the flammable nature of LPG poses significant risks, particularly in the event of leaks, which can lead to explosions and fires. This research aims to develop a system that monitors and detects gas leaks in real-time to prevent such hazardous incidents. The proposed system utilizes Internet of Things (IoT) technology, incorporating MQ-6 gas sensors and Raspberry Pi to detect LPG leaks. The MQ-6 sensors are capable of identifying the presence of gas, while the Raspberry Pi processes the data and sends notifications in the event of a leak. The methodology includes literature reviews, user interviews, and data analysis to design an effective monitoring system. The results indicate that the system can accurately detect gas leaks and provide real-time alerts via SMS or a mobile application. In conclusion, this study demonstrates that an IoT-based monitoring and detection system for LPG leaks can significantly enhance safety by enabling prompt responses to gas leaks. This system not only benefits users by facilitating quicker leak management but also contributes to broader safety measures in residential and commercial environments.
Co-Authors Arwan, Asep Sanudin Sanudin Turmudi, Ahmad Zy, Ahmad Turmudi