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Brilliant, Brian
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering

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Sistem Deteksi Masker Wajah Menggunakan CNN untuk Akses Pintu Otomatis Firizki, Muh.; Brilliant, Brian; Warohma, Ayu Mawadda
MDP Student Conference Vol 5 No 2 (2026): The 5th MDP Student Conference 2026
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v5i2.14413

Abstract

This research discusses the development of a face mask detection system using a Convolutional Neural Network (CNN) for automatic door access in hospitals. Considering the high risk of infectious disease transmission in hospital environments, the implementation of strict health protocols, including mandatory mask usage, is essential. Manual supervision of mask compliance has limitations; therefore, an automated system is required to improve monitoring effectiveness. The dataset used in this study was collected using an ESP32 Cam, consisting of 1,186 images of masked and unmasked faces. The CNN model achieved an average training accuracy of 96.60%, with Precision and Recall values of 0.98. The automatic door system was evaluated through real-time testing involving six subjects, each undergoing 15 trials with masks and 15 trials without masks, resulting in a total of 180 trials. The system achieved a detection accuracy of 90.00% for masked faces and 74.44% for unmasked faces, with an overall system accuracy of 82.22%. These results indicate that the proposed system is capable of reliably supporting automatic door access control based on face mask compliance in hospital environments.
Evaluasi Kinerja Sistem IoT Berbasis ESP32 dan MQTT untuk Pemantauan Kelembapan Ruangan Brilliant, Brian; Rahmadani, Ferliana; -, Helsen; Firmansyah, M. Ilham; Saputra, M. Riski Tri; Frey, Norick Christian; Sopyan, Rafly Isfandyar; Rahman, Abdul
MDP Student Conference Vol 5 No 2 (2026): The 5th MDP Student Conference 2026
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v5i2.15226

Abstract

This study evaluates the performance of an ESP32 and MQTT-based IoT system for real-time humidity monitoring and regulation within a container. The system is developed by integrating a DHT22 sensor, relay module, and exhaust fan. Using a quantitative approach, this research measures system performance parameters, including sampling interval, Packet Delivery Ratio (PDR), and latency. The test results show an average sampling interval of 20.16 seconds with a 0.8% deviation from the ideal value. The achieved PDR level is 99.2%, with an average system latency of 725 ms. Based on the test results, the exhaust fan is capable of automatically reducing humidity when the sensor detects values above the 65% set point. Overall, the system demonstrates good reliability in maintaining stable data transmission and providing rapid control responses via the Virtuino IoT interface.
Co-Authors -, Helsen Abdul Rahman Firizki, Muh. Firmansyah, M. Ilham Frey, Norick Christian Rahmadani, Ferliana Saputra, M. Riski Tri Sopyan, Rafly Isfandyar Warohma, Ayu Mawadda