Despite its high accuracy to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach possesses several limitations (e.g., the lengthy invasive procedure, the reagent availability, and the requirement of specialized laboratory, equipment, and trained staffs). We developed and employed a low-cost, noninvasive method to rapidly sniff out the coronavirus disease 2019 (COVID-19) based on a portable electronic nose (GeNose C19) integrating metal oxide semiconductor gas sensor array, optimized feature extraction, and machine learning models. This approach was evaluated in profiling tests involving a total number of 615 breath samples (i.e., 333 positive and 282 negative COVID-19 confirmed by RT-qPCR) obtained from 83 patients in two hospitals located in the Special Region of Yogyakarta, Indonesia. Four different machine learning algorithms (i.e., linear discriminant analysis (LDA), support vector machine (SVM), stacked multilayer perceptron (MLP), and deep neural network (DNN)) were utilized to identify the top-performing pattern recognition methods and to obtain high system detection accuracy (88–95%), sensitivity (86–94%), specificity (88–95%) levels from the testing datasets. Our results suggest that GeNose C19 can be considered a highly potential breathalyzer for fast COVID-19 screening.

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Fever is one of the initial presentations that a suspect of COVID-19 might have. Fever is indicated by body temperature higher than normal, which is more than 37.12°C. A thermometer gun is one device that is utilized to measure body temperature. But it requires a short line-of-sight distance between the device and the subjects (< 30 cm). In public facilities like shops, malls, schools, colleges, hospitals, and airports, the device’s use can initiate crowded or queue that higher the COVID-19 infection potential. In this research, wearable glasses is designed to replace such device. The prototype was built to display the thermal-map and body temperature of a single suspect. It can measure body temperature up to 2.5 meters. Based on the evaluation, the average error was about 0.57°C. Recalling that the used thermal array sensor’s inaccuracy is ±2.5°C, then the prototyping has a high potential for further use.

Tujuan dari penelitian ini adalah mengoptimasi kinerja system E-Nose dengan melakukan seleksi fitur untuk memperoleh kombinasi fitur yang terbaik dalam mengklasifikasi aroma jenis kopi arabika Gayo. Kopi ini merupakan salah satu kopi spesial dari Indonesia yang berasal dari Provinsi Aceh. Berbagai faktor dapat mempengaruhi hasil akhir kopi salah satunya pada proses pengolahan pasca panen diantaranya teknik proses kering (drying) dengan metode Natural dan Wine. Perbedaan metode pengolahan pasca panen ini dapat mempengaruhi aroma kopi yang dihasilkan dari setiap kopi yang memiliki aroma dan cita rasa yang khas. Penerapan sistem Electronic Nose (E-Nose) dapat diaplikasikan untuk mengklasifikasi aroma yang berbeda dari jenis kopi Gayo natural dan Gayo wine, namun kesamaan respon sensor dan banyaknya data menyebabkan kurang spesifik dan menurunkan performa kinerja sistem. Implementasi seleksi fitur dapat diterapkan pada proses klasifikasi dengan menggunakan metode Support Vector Machine (SVM) berdasarkan jumlah galat Sum of Absolute Errors (SAE) untuk mendapatkan kombinasi fitur terbaik sehingga mendapatkan kinerja sistem yang lebih optimal. Hasil penelitian ini mendapatkan 5 fitur terbaik dengan nilai akurasi sebesar 93,33%, presisi sebesar 93,33% dan sensitivitas sebesar 93,33%.

Kombucha tea is a fermented tea drink using Symbiotic Culture of Bacteria and Yeast (SCOBY), which is currently being widely produced and consumed because of its health benefits. Kombucha tea has a great opportunity on a global industrial scale, so it is necessary to monitor the production process. This paper uses system sensor gas array or eNose to distinguish volatiles delivered amid the maturation preparation and to think about the method stages using the linear discriminant analysis (LDA) strategy. The results obtained from LDA showed that the 1st to 6th day was the growth process of the SCOBY mushroom, while the 7th to 12th day was the ripening process of kombucha to be consumed. The stages of the kombucha fermentation process are classified using three classification methods, namely KNN, CART, and LDA. The results show the highest accuracy obtained by LDA, with an accuracy of 83.33%. These results can be agreed that eNose can be used as a measuring tool for monitoring the fermentation process and testing the quality of kombucha tea.