Received: January 4, 2022. Revised: January 27, 2022. 394 International Journal of Intelligent Engineering and Systems, Vol.15, No.2, 2022 DOI: 10.22266/ijies2022.0430.36 A New Approach for Detection of Viral Respiratory Infections Using E-nose Through Sweat from Armpit with Fully Connected Deep Network Malikhah Malikhah 1 Riyanarto Sarno 1 * Sozo Inoue 2 M. Syauqi Hanif Ardani 1 Doni Putra Purbawa 1 Shoffi Izza Sabilla 1 Kelly Rossa Sungkono 1 Chastine Fatichah 1 Dwi Sunaryono 1 Arief Bakhtiar 3 Libriansyah 4 Cita RS. Prakoeswa 5 Damayanti Tinduh 6 Yetti Hernaningsih 7 1 Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember (ITS) Sukolilo, Surabaya, Indonesia 2 Department of Human Intelligence Systems, Kyushu Institute of Technology Kitakyushu, Japan 3 Department of Pulmonology, Faculty of Medicine, Airlangga University, Surabaya, Indonesia 4 Department of Internal Medicine, Dr. Ramelan Navy Hospital Surabaya, Indonesia 5 Department of Dermatology and Venerology, Faculty of Medicine, Airlangga University, Surabaya, Indonesia 6 Department of Physical Medical and Rehabilitation, Faculty of Medicine, Airlangga University, Surabaya, Indonesia 7 Department of Clinical Pathology, Faculty of Medicine, Airlangga University, Surabaya, Indonesia * Corresponding author’s Email: riyanarto@if.its.ac.id Abstract: Viral respiratory infections are the most common diseases suffered by all age groups worldwide. The gold standard for diagnosing viral respiratory infection is through the molecular method, but this diagnosis is expensive, requires sophisticated equipment, can only be performed by well-trained medical staff, and is painful. Volatile Organic Compounds (VOCs) are compounds released from the human body that can be a marker of disease and based on numerous studies it also contains VOCs. An electronic nose (E-nose) is a device that can be used to identify disease. This study proposes a new approach for the detection of viral respiratory infections through sweat from the armpit using an E-nose consisting of 5 semiconductor gases and a single-board computer. Several statistical parameters are used to obtain features and the detection algorithm used is Fully Connected Deep Network (FCDN). Several sizes of hidden layers were tested to obtain the best FCDN model. This study also proposes the selection of the best FCDN model which is a trade-off between complexity and accuracy, so that the model stored in E-nose is a model that not only has good accuracy but is also not too complex. The experimental results show that using 29 statistical parameters and 2 hidden layers generate the highest accuracy of 0.940 for the detection of 2 classes, namely positive and negative, with sensitivity and specificity of 0.967 and 0.915, respectively, where the best FCDN model has a total of 90,561 parameters. Keywords: Armpit, Electronic nose, Deep learning, Sweat, Statistical parameters, Viral respiratory infections. 1. Introduction Viral respiratory infections are the most common diseases suffered by all age groups worldwide. Elderly people and children have the highest risk of being infected with the disease, especially in low- income countries. A particular report states that more than 16 % of deaths occur in children under 5 years old [1]. The spread of this disease is rapid, through direct physical contact, droplets, or aerosols. Quick and accurate diagnosis is needed so that the subject receives the right treatment and minimizes the spread of the disease. The gold standard for diagnosing this disease is through the molecular method, but this diagnosis is expensive, requires sophisticated equipment, can only be performed by well-trained medical staff, and is painful [2]. Volatile organic compounds or better known by