Gas Detection and Classification Using Neural Network Based Gas Sensors

Alcoholic beverages, apart from being haram, also cause loss of consciousness. The influence of alcohol while driving is very dangerous and can result in an accident. For this reason, it is necessary to detect the alcohol content in beverages so that their halal status is known and to avoid the dang...

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Main Authors: Munaf Ismail, Sri Arttini Dwi Prasetyowati
Format: Article
Language:English
Published: Universitas Syiah Kuala 2023-07-01
Series:Jurnal Rekayasa Elektrika
Subjects:
Online Access:https://jurnal.usk.ac.id/JRE/article/view/30974
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author Munaf Ismail
Sri Arttini Dwi Prasetyowati
author_facet Munaf Ismail
Sri Arttini Dwi Prasetyowati
author_sort Munaf Ismail
collection DOAJ
description Alcoholic beverages, apart from being haram, also cause loss of consciousness. The influence of alcohol while driving is very dangerous and can result in an accident. For this reason, it is necessary to detect the alcohol content in beverages so that their halal status is known and to avoid the dangers of consuming alcohol. This research is to detect the aroma of alcohol using the MQ-3 gas sensor, which consists of an aroma sensor in general with an Artificial Neuron Network (ANN), such as the number of neurons, layers, and epoch. Most of the learning schemes require testing to optimize the model structure. For this experiment, ANN is used as a liquid classification in grouping alcoholic and non-alcoholic liquids. The MQ-3 gas sensor successfully reads liquid vapor in alcohol with levels of 30%, 50%, 70%, and other water-based liquids. An artificial neural network with 2 hidden layers, 10 neurons, and 1000 iterations with the sigmoid activation function can approach a regression score of 1.1545 and sq error score of 0.5781.
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spelling doaj.art-c575826928134260b393f4550187c2e92023-08-17T15:53:13ZengUniversitas Syiah KualaJurnal Rekayasa Elektrika1412-47852252-620X2023-07-0119210.17529/jre.v19i2.3097416451Gas Detection and Classification Using Neural Network Based Gas SensorsMunaf Ismail0Sri Arttini Dwi Prasetyowati1Universitas Islam Sultan AgungUniversitas Islam Sultan AgungAlcoholic beverages, apart from being haram, also cause loss of consciousness. The influence of alcohol while driving is very dangerous and can result in an accident. For this reason, it is necessary to detect the alcohol content in beverages so that their halal status is known and to avoid the dangers of consuming alcohol. This research is to detect the aroma of alcohol using the MQ-3 gas sensor, which consists of an aroma sensor in general with an Artificial Neuron Network (ANN), such as the number of neurons, layers, and epoch. Most of the learning schemes require testing to optimize the model structure. For this experiment, ANN is used as a liquid classification in grouping alcoholic and non-alcoholic liquids. The MQ-3 gas sensor successfully reads liquid vapor in alcohol with levels of 30%, 50%, 70%, and other water-based liquids. An artificial neural network with 2 hidden layers, 10 neurons, and 1000 iterations with the sigmoid activation function can approach a regression score of 1.1545 and sq error score of 0.5781.https://jurnal.usk.ac.id/JRE/article/view/30974alcohol gasgas sensorartificial neuron network
spellingShingle Munaf Ismail
Sri Arttini Dwi Prasetyowati
Gas Detection and Classification Using Neural Network Based Gas Sensors
Jurnal Rekayasa Elektrika
alcohol gas
gas sensor
artificial neuron network
title Gas Detection and Classification Using Neural Network Based Gas Sensors
title_full Gas Detection and Classification Using Neural Network Based Gas Sensors
title_fullStr Gas Detection and Classification Using Neural Network Based Gas Sensors
title_full_unstemmed Gas Detection and Classification Using Neural Network Based Gas Sensors
title_short Gas Detection and Classification Using Neural Network Based Gas Sensors
title_sort gas detection and classification using neural network based gas sensors
topic alcohol gas
gas sensor
artificial neuron network
url https://jurnal.usk.ac.id/JRE/article/view/30974
work_keys_str_mv AT munafismail gasdetectionandclassificationusingneuralnetworkbasedgassensors
AT sriarttinidwiprasetyowati gasdetectionandclassificationusingneuralnetworkbasedgassensors