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Prediction of Carbon Monoxide Concentration with Variation of Support Vector Regression Kernel Parameter Value

Journal of Mathematical and Fundamental Sciences · Vol. 54 No. 1 (2022) · hlm. 39–53

DOI
10.5614/j.math.fund.sci.2022.54.1.3
ISSN
2338-5510, 2337-5760
Terbit
2022-03-02
Penerbit
Directorate for Research and Innovation (DRI) ITB
Bahasa
eng

Abstrak

Human and industrial activities produce air pollutants that can cause a decline in air quality. In urban areas, transportation activities are the main source of air pollution. One of the emitted air pollutants produced by transportation is carbon monoxide (CO). The understanding of CO concentration is crucial since its overabundance beyond a certain limit will have a negative impact on human health and the environment. In this study, the support vector regression (SVR) method was used to predict CO concentration. The purpose of this study was to predict the hourly CO concentration in the Ujung Berung district, Bandung City, West Java, Indonesia with optimal prediction accuracy. An experiment was carried out by modeling the CO concentration with varying kernel parameter values to obtain accurate prediction results. The suitability of the values between error (ɛ), a trade-off constant (C), and variation mismatch (γ) is vital to obtain optimal prediction results. The results showed that the best prediction accuracy value was 97.68% with kernel parameter values ɛ = 0.02, γ = 30, and C = 0.006. These results may lead to proper decision making on environmental issues and can improve air pollution control strategies.

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  • Bandung Institute of Technology
  • IPB University
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Rekan penulisOrganisasiArtikel bersama
Mitra DjamalBandung Institute of Technology3
Irwan MeilanoAfiliasi tidak diketahui3
Anton IrawanAfiliasi tidak diketahui3
Yogi Wibisono BudhiAfiliasi tidak diketahui3
S. SubagjoAfiliasi tidak diketahui3
Doddy SutarnoAfiliasi tidak diketahui3
Hendra GunawanAfiliasi tidak diketahui2
Ratih KumalasariAfiliasi tidak diketahui2
Teguh KurniawanAfiliasi tidak diketahui2
Hafid AlwanAfiliasi tidak diketahui2
Tjokorde Walmiki SamadhiAfiliasi tidak diketahui2
M. EffendyAfiliasi tidak diketahui2
Yogi W. BudhiAfiliasi tidak diketahui2
Eleonora AgustineAfiliasi tidak diketahui2
R. KurniaBandung Institute of Technology1
Suprijadi SuprijadiBandung Institute of Technology1
Melany FebrinaAfiliasi tidak diketahui1
Eko SatriaAfiliasi tidak diketahui1
Martin LiessAfiliasi tidak diketahui1
Endang WarsikiAfiliasi tidak diketahui1
Elfa Susanti ThamrinAfiliasi tidak diketahui1
Ono SuparnoAfiliasi tidak diketahui1
Husnul HamdiAfiliasi tidak diketahui1
Rustan RustanAfiliasi tidak diketahui1
Ramadhani Eka PutraAfiliasi tidak diketahui1
Nadia Damika PutriAfiliasi tidak diketahui1
Asep Bayu Dani NandiyantoAfiliasi tidak diketahui1
Darisman DarismanAfiliasi tidak diketahui1
Dina PujiantiAfiliasi tidak diketahui1
Muhammad Saifullah Abu BakarAfiliasi tidak diketahui1
Ari SetiawanAfiliasi tidak diketahui1
Irzaman IrzamanIPB University1
Imam SuyantoAfiliasi tidak diketahui1
Heriansyah PutraAfiliasi tidak diketahui1
Herlan DarmawanAfiliasi tidak diketahui1
Wahyudi WahyudiAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jmfs/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Prediction of Carbon Monoxide Concentration with Variation of Support Vector Regression Kernel Parameter Value</dc:title>
	<dc:creator>Ernwati, Halawa</dc:creator>
	<dc:creator>Bindar, Yazid</dc:creator>
	<dc:creator>Purqon, Acep</dc:creator>
	<dc:creator>Srigutomo, Wahyu</dc:creator>
	<dc:subject xml:lang="en-US">air pollution</dc:subject>
	<dc:subject xml:lang="en-US">carbon monoxide</dc:subject>
	<dc:subject xml:lang="en-US">kernel</dc:subject>
	<dc:subject xml:lang="en-US">prediction</dc:subject>
	<dc:subject xml:lang="en-US">support vector regression</dc:subject>
	<dc:description xml:lang="en-US">Human and industrial activities produce air pollutants that can cause a decline in air quality. In urban areas, transportation activities are the main source of air pollution. One of the emitted air pollutants produced by transportation is carbon monoxide (CO). The understanding of CO concentration is crucial since its overabundance beyond a certain limit will have a negative impact on human health and the environment. In this study, the support vector regression (SVR) method was used to predict CO concentration. The purpose of this study was to predict the hourly CO concentration in the Ujung Berung district, Bandung City, West Java, Indonesia with optimal prediction accuracy. An experiment was carried out by modeling the CO concentration with varying kernel parameter values to obtain accurate prediction results. The suitability of the values between error (ɛ), a trade-off constant (C), and variation mismatch (γ) is vital to obtain optimal prediction results. The results showed that the best prediction accuracy value was 97.68% with kernel parameter values ɛ = 0.02, γ = 30, and C = 0.006. These results may lead to proper decision making on environmental issues and can improve air pollution control strategies.</dc:description>
	<dc:publisher xml:lang="en-US">Directorate for Research and Innovation (DRI) ITB</dc:publisher>
	<dc:date>2022-03-02</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jmfs/article/view/14838</dc:identifier>
	<dc:identifier>10.5614/j.math.fund.sci.2022.54.1.3</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Mathematical and Fundamental Sciences; Vol. 54 No. 1 (2022); 39-53</dc:source>
	<dc:source>2338-5510</dc:source>
	<dc:source>2337-5760</dc:source>
	<dc:source>10.5614/j.math.fund.sci.2022.54.1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jmfs/article/view/14838/5674</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Journal of Mathematical and Fundamental Sciences</dc:rights>
</oai_dc:dc>