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All Journal JFA (Jurnal Fisika dan Aplikasinya)
Farghani, Muhammad Agung
Department of Electrical Engineering and Informatics, Vocational College, UGM, Yogyakarta, Indonesia
Physics

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Comparison of Supervised Learning Algorithms for Cigarette and Vape Smoke Classification Using Electronic Nose Farghani, Muhammad Agung; Wijayakusuma, Nurul Izzah; Sumanto, Budi
Jurnal Fisika dan Aplikasinya Vol 20, No 3 (2024)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat, LPPM-ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24604682.v20i3.17939

Abstract

This research discusses applying the Supervised Learning method using Electronic Nose to classify the types of cigarette and vape smoke in the air. Electronic Nose is used as a scent detector that can identify the characteristics of smoke from both sources. Three Supervised Learning algorithms, namely KNN, SVM, and Decision Tree, were applied to compare the performance in classifying smoke types. The data comprised reference air samples, air contaminated by manufactured cigarette smoke, rolled cigarettes, and vape. The results showed that all three Supervised Learning algorithms successfully provided an excellent classification for cigarette and vape smoke types using data from Electronic Nose. The best accuracy result was achieved by SVM, with an accuracy rate of  96.55%. This research contributes to identifying sources of air pollution that have the potential to endanger human health
Co-Authors Budi Sumanto Wijayakusuma, Nurul Izzah