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COMBINATION OF KNN AND PARTICLE SWARM OPTIMIZATION (PSO) ON AIR QUALITY PREDICTION

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 16 No. 1 (2022) · pp. 007–014

DOI
10.30598/barekengvol16iss1pp007-014
ISSN
2615-3017, 1978-7227
Published
2022-03-21
Publisher
PATTIMURA UNIVERSITY
Language
eng

Abstract

The increase in the use of energy sources causes air pollution. The Air Pollutant Index (API) is information about the air quality of a place and at a certain time. API has several parameters, namely SO2, PM10, NO2, O3, and CO. In this study, the KNN method was used to assist categorize air quality. However, all training data were used during the classification process with KNN causes a long prediction process. Another problem with KNN is difficult to determine the optimal value of the K parameter in KNN. The Particle Swarm Optimization (PSO) method can be used for problems on KNN. Therefore, the aim of this study is to predict air quality based on the API by combining the KNN-PSO method. The dataset used is the API dataset for the DKI Jakarta area 2017-2019 totaling 1075 data. The results showed the accuracy for the KNN-PSO method was 98.42% with a precision value of 97.75% and a recall value of 98.13%. To further analyze the results on the combined method, the results of this study were compared with the KNN method only. The results obtained from the KNN method are lower than the KNN-PSO method. So it can be concluded that the KNN-PSO method is great and robust in air quality classification or prediction.

Collaboration network of the authors

24 authors · 68 co-authorship links · 19 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Sriwijaya University
  • Politeknik Negeri Lhokseumawe
  • Padjadjaran University
  • Tadulako University
  • Indonesia University of Education
  • Universitas Gadjah Mada
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Irmeilyana IrmeilyanaSriwijaya University6
Yuli AndrianiPadjadjaran University3
Dwi FitriantiSriwijaya University3
Henny Nur SyafitriSriwijaya University3
Desty RodiahSriwijaya University2
Annisa KartikasariAffiliation unknown2
Wahyudin WahyudinTadulako University1
Sufyani PrabawantoIndonesia University of Education1
Hapizah HapizahSriwijaya University1
Budi MulyonoSriwijaya University1
Sri Indra MaiyantiSriwijaya University1
Ngudiantoro NgudiantoroSriwijaya University1
Bambang SuprihatinSriwijaya University1
Budi MulyonoAffiliation unknown1
Echa Alda MeliniaAffiliation unknown1
Teguh Bharata AdjiUniversitas Gadjah Mada1
Noor Akhmad SetiawanAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">COMBINATION OF KNN AND PARTICLE SWARM OPTIMIZATION (PSO) ON AIR QUALITY PREDICTION</dc:title>
	<dc:creator>Yahdin, Sugandi</dc:creator>
	<dc:creator>Desiani, Anita</dc:creator>
	<dc:creator>Andhini, Shania Putri</dc:creator>
	<dc:creator>Cahyawati, Dian</dc:creator>
	<dc:creator>Primartha, Rifkie</dc:creator>
	<dc:creator>Arhami, Muhammad</dc:creator>
	<dc:creator>Arinda, Ditia Fitri</dc:creator>
	<dc:description xml:lang="en-US">The increase in the use of energy sources causes air pollution. The Air Pollutant Index (API) is information about the air quality of a place and at a certain time. API has several parameters, namely SO2, PM10, NO2, O3, and CO. In this study, the KNN method was used to assist categorize air quality. However, all training data were used during the classification process with KNN causes a long prediction process. Another problem with KNN is difficult to determine the optimal value of the K parameter in KNN. The Particle Swarm Optimization (PSO) method can be used for problems on KNN. Therefore, the aim of this study is to predict air quality based on the API by combining the KNN-PSO method. The dataset used is the API dataset for the DKI Jakarta area 2017-2019 totaling 1075 data. The results showed the accuracy for the KNN-PSO method was 98.42% with a precision value of 97.75% and a recall value of 98.13%. To further analyze the results on the combined method, the results of this study were compared with the KNN method only. The results obtained from the KNN method are lower than the KNN-PSO method. So it can be concluded that the KNN-PSO method is great and robust in air quality classification or prediction.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2022-03-21</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/3505</dc:identifier>
	<dc:identifier>10.30598/barekengvol16iss1pp007-014</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan; 007-014</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol16iss1year2022</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/3505/3995</dc:relation>
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