Electronic Nose in Classification of Gas Sensor Array Detection Through Flow Modulation

The sensor array consists of several sensors whose number depends on the analysis of a case to be generated. The electronic nose (e-nose) is similar to how the human sense of smell works. This technology is quite good at detecting gases that can harm human health when humans smell them. This paper utilizes an array of sensors as an e-nose in detecting 12 classes of gases and their concentrations, consisting of acetone 0.1, a mixture of acetone 0.1 and ethanol 0.1, a mixture of acetone 0.1 and ethanol 0.3, a combination of acetone 0.1 and ethanol 1, acetone 0.3, a mixture of acetone 0.3 and ethanol 0.1, acetone 1, a mixture of acetone 1 and ethanol 0.1, air, ethanol 0.1, ethanol 0.3, and ethanol 1. The input feature data used in this gas flow modulation system uses 16 sensors consisting of the Taguchi Gas Sensors (TGS). The dataset used in this paper uses an open dataset to obtain the accuracy and R² score of the classification model. The classification model applied, in this case, is Gaussian Naive Bayes (GNB), Decision Tree Classifier (DTC), K-Nearest Neighbors (KNN), Random Forest Classifier (RFC), Ada Boost Classifier (ABC), Gradient Boosting Classifier (GBC), Support Vector Classifier (SVC), Neural Network Multi-Layer Perceptron Classifier (NN-MLPC), Gaussian Process Classifier (GPC), Quadratic Discriminant Analysis (QDA), and Bagging Classifier (BC). The best metric result obtained in this classification model is RFC which accuracy and R² score can produce a value of 100% each.