Development of Urine Alcohol Content Predicting System Using Machine Learning Based on the Electronic Nose

Authors

  • Hanis Amalia Saputri Computer Science Department, Binus Graduate Program, Master of Computer of Science, Bina Nusantara University, Jakarta, Indonesia, 11480 https://orcid.org/0000-0002-4068-9653
  • Alexander Agung Santoso Gunawan Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia, 11480 https://orcid.org/0000-0002-1097-5173
  • Izzi Dzikri Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia, 11480

Keywords:

electronics nose, urine alcohol content, regression model machine learning

Abstract

Because many countries still rely on alcohol due to strong drinking cultural practices, efforts to minimize its detrimental usage are more difficult even though alcohol can affect public health. As a result, a device to track alcohol consumption is needed. This research developed an electronic nose based on Internet of Things (IoT) as a new potential tool for measuring the alcohol content in the human body from urine odor. The electronic nose prototype used three gas sensors and was tested using simulated urine that had nine predetermined alcohol contents. We developed several regression models for predicting urine alcohol content using machine learning algorithms, including linear and non-linear algorithms. Based on our experiments, we can reach satisfied results with Support Vector Regression (SVR) (MSE = 0.009) and Random Forest (MSE = 0.014). The results make the electronic nose prototype suitable for measuring the alcohol content in urine.

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The Sensors response to each sample

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Published

17.02.2023

How to Cite

Amalia Saputri, H. ., Santoso Gunawan, A. A. ., & Dzikri, I. . (2023). Development of Urine Alcohol Content Predicting System Using Machine Learning Based on the Electronic Nose. International Journal of Intelligent Systems and Applications in Engineering, 11(2), 449–453. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2653

Issue

Vol. 11 No. 2 (2023)

Section

Research Article

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