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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Generic Jurnal Elektronika dan Telekomunikasi
Bhakti Yudho Suprapto
Universitas Sriwijaya
Computer Science & IT Electrical & Electronics Engineering Engineering

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Facial recognition and body temperature measurements based on thermal images using a deep-learning algorithm Suci Dwijayanti; Muhammad Ridho Ramadhan; Bhakti Yudho Suprapto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1654-1665

Abstract

Recognizing the early symptoms of the SARS-CoV-2 virus (COVID-19) is essential for minimizing its spread. One of the typical symptoms of a person infected with COVID-19 is increased body temperature beyond the normal range. Facial recognition can be used to separate healthy people from those with high body temperatures based on thermal images of the faces. In this study, the XEAST XE-27 thermal imager modes 2, 3, and 4 comprising 1500 thermal images each were compared. The facial recognition was performed using a convolutional neural network. Additionally, body temperatures were extracted from thermal images using matrix laboratory (MATLAB) by considering the minimum and maximum temperatures of each mode and class. The network training results indicate that the accuracies achieved by the proposed facial recognition system in modes 2, 3, and 4 are 87.33%, 92.33%, and 91.66%, respectively. Furthermore, the accuracies of body temperature extraction in modes 2, 3, and 4 are 70%, 60%, and 40%, respectively. Thus, the proposed system serves as a contactless technique for the early detection of COVID-19 symptoms by combining facial recognition and body temperature measurements.
Designing Human-Robot Communication in the Indonesian Language Using the Deep Bidirectional Long Short-Term Memory Algorithm Suci Dwijayanti; Ahmad Reinaldi Akbar; Bhakti Yudho Suprapto
Jurnal Elektronika dan Telekomunikasi Vol. 24 No. 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.595

Abstract

Humanoid robots closely resemble humans and engage in various human-like activities while responding to queries from their users, facilitating two-way communication between humans and robots. This bidirectional interaction is enabled through the integration of speech-to-text and text-to-speech systems within the robot. However, research on two-way communication systems for humanoid robots utilizing speech-to-text and text-to-speech technologies has predominantly focused on the English language. This study aims to develop a real-time two-way communication system between humans and a robot, with data collected from ten respondents, including eight males and two females. The sentences used adhere to the standard rules of the Indonesian language. The speech-to-text system employs a deep bidirectional long short-term memory algorithm, coupled with feature extraction via the Mel frequency cepstral coefficients, to convert spoken language into text. Conversely, the text-to-speech system utilizes the Python pyttsx3 module to translate text into spoken responses delivered by the robot. The results indicate that the speech-to-text model achieves a high level of accuracy under quiet-room conditions, with noise levels ranging from 57.5 to 60 dB, boasting an average word error rate (WER) of 24.99% and 25.31% for speakers within and outside the dataset, respectively. In settings with engine noise and crowds, where noise levels range from 62.4 to 86 dB, the measured WER is 36.36% and 36.96% for speakers within and outside the dataset, respectively. This study demonstrates the feasibility of implementing a two-way communication system between humans and a robot, enabling the robot to respond to various vocal inputs effectively. 
Co-Authors A. Wahyudin Agung Juli Anda Agus Adhi Nugroho Ahmad Reinaldi Akbar Ahmad Zarkasi Aulia Ghaida Bustanul Arifin Deris Stiawan Eka Nuryanto Budi Susila Erliza Yuniarti Fachrudin Abdau Hera Hikmarika Huda Ubaya Muhamad Haddin Muhammad Khosyi'in Muhammad Khosyi'in Muhammad Ridho Ramadhan Osvari Arsalan Siti Nurmaini Sri Arttini Dwi Prasetyawati Sri Desy Siswanti Suci Dwijayanti Yesi Novaria Kunang Zainuddin Nawawi