Penilaian Kualitas Udara dan Polusi Menggunakan Algoritma Support Vector Machine

Authors

  • Ridho Sholehurrohman Jurusan Ilmu Komputer, Universitas Lampung
  • Ivani Valentine Jurusan Magister Teknik Informatika, Institut Informatika dan Bisnis Darmajaya

DOI:

https://doi.org/10.33020/saintekom.v15i1.810

Keywords:

Air Quality, Air Pollution, SVM Algorithm

Abstract

Good air quality is essential for healthy and sustainable living, but increasing air pollution caused by industrialization, urbanization, and motor vehicles has become a serious global issue. Air pollution negatively affects health, the environment, and the quality of life, making air quality monitoring and assessment a priority. The complexity of air quality data renders conventional analytical approaches less effective; therefore, machine learning methods such as Random Forest and Neural Networks have been applied to address these challenges. However, these methods have limitations in handling non-linear patterns or computational efficiency. This study employs the Support Vector Machine (SVM) algorithm with various kernels to classify air quality based on pollution and environmental parameters into categories of Good, Moderate, Poor, and Hazardous. The results indicate that the Polynomial Kernel performs best for the Good category, while the RBF Kernel is also competitive but less optimal for the Hazardous and Poor categories. With parameter optimization using GridSearchCV, the combination of C=10 and Gamma=0.1 or scale achieved an average accuracy of 90.75%. CO concentration and proximity to industrial areas proved to be significant features in classification. This study aims to support air pollution management and mitigate its impacts on society.

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References

Addis Bikis, “Urban Air Pollution and Greenness in Relation to Public Health,“ Mizan-Tepi University, Tepi, Ethiopia. January 2023. https://doi.org/10.1155/2023/8516622

Abdulwaheed Tella, Abdul-Lateef Balogun, Naheem Adebisi, and Samsuri Abdullah, "Spatial assessment of PM10 hotspots using Random Forest, K-Nearest Neighbour and Naïve Bayes", Atmospheric Pollution Research. Volume 12, Issue 10, October 2021. https://doi.org/10.1016/j.apr.2021.101202

Biao Sun, Chuanglin Fang, Xia Liao, Xiaomin Guo, and Zhitao Liu, "The relationship between urbanization and air pollution affected by intercity factor mobility: A case of the Yangtze River Delta region" Environmental Impact Assessment Review, Volume 100, May 2023. https://doi.org/10.1016/j.eiar.2023.107092

B.M. Cintoro, S.A. Mulya, R. Sulistyorini and D. herianto, "Analisis emisi polutan kendaraan akibat kegiatan transportasi di lingkungan Universitas Lampung, " Rekayasa: Jurnal Ilmiah Fakultas Teknik Universitas Lampung, 27(3), Desember 2023, 22-24.

Burges CJ. A tutorial on Support Vector Machines for pattern recognition. Data Min Knowledge Discovery 1998;2(2):121–67.

Burges CJ, Scho¨lkopf B. Improving the accuracy and speed of Support Vector Machines. Neural Inform Proc Syst 1997;9:375–81

Chaudhary, M.T. “Urban Air Pollution, Urban Heat Island and Human Health: A Review of the Literature,“ Sustainability 2022, 14, 9234. https://doi.org/10.3390/su14159234

E.A. Zanaty, "Support Vector Machines (SVMs) versus Multilayer Perception (MLP) in data classification", Egyptian Informatics Journal, Volume 13, Issue 3, November 2012, Pages 177-183. https://doi.org/10.1016/j.eij.2012.08.002

IQAIR: Anggota Well Keystone (2023) https://www.iqair.com/id/world-most-polluted-cities.

Julien Vachon, Jules Kerckhoffs, Stéphane Buteau, and Audrey Smargiassi, “Do machine learning methods improve prediction of ambient air pollutants with high spatial contrast? A systematic review,“ Environmental Research, Volume 262, Part 2, 1 December 2024. https://doi.org/10.1016/j.envres.2024.119751

Kamel Maaloul, and Lejdel Brahim, "Comparative Analysis of Machine Learning for Predicting Air Quality in Smart Cities", WSEAS TRANSACTIONS on COMPUTERS, Volume 21, 2022. http://dx.doi.org/10.37394/23205.2022.21.30

Pedro Guerra, José Manuel Bravo, Ari Ingimundarson, and Vicenç Puig, “Robust Fault Detection Based on Zonotope-Based Set-Membership Parameter Consistency Test”, A Proceedings Volume from the 6th IFAC Symposium, SAFEPROCESS 2006, Beijing, P.R. China, August 30–September 1, 2006. https://doi.org/10.1016/B978-008044485-7/50178-0

Rameswar Debnath, and Haruhisa Takahashi, "Kernel selection for the Support Vector Machine", IEICE Transactions on Information and Systems, Vol. E87-D, No 12, Desember 2004.

Scho¨lkopf B, Burges C, Smola A. Advances in kernel methods support vector learning. MIT Press; 1998.

Scho¨lkopf BS, Mika CJ, Burges P, Knirsch RR, Muller KG. Input space versus feature space in kernel-based methods. IEEE Trans Neural Network 1999;10(5):1000–17

Scho¨lkopf B, and Smola AJ. “Learning with kernels”. Cambridge, MA: The MIT Press; 2002.

Vishal Sharma, et al., “Insights into the recent advances of agro-industrial waste valorization for sustainable biogas production”, Bioresource Technology, Volume 390, December 2023. https://doi.org/10.1016/j.biortech.2023.129829

Wardatus Sholihah, Ade Silvia Handayani, and Sarjana, "Revolutionizing Healthcare: Comprehensive Evaluation and Optimization of SVM Kernels for Precise General Health Diagnosis", Scientific Journal of Informatics, Vol. 10, No. 4, Nov 2023. doi: https://journal.unnes.ac.id/nju/index.php/sji/article/view/46430

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Published

31-03-2025

How to Cite

Sholehurrohman, Ridho, and Ivani Valentine. 2025. “Penilaian Kualitas Udara Dan Polusi Menggunakan Algoritma Support Vector Machine”. Jurnal Saintekom : Sains, Teknologi, Komputer Dan Manajemen 15 (1):56-68. https://doi.org/10.33020/saintekom.v15i1.810.

Issue

Vol. 15 No. 1 (2025): Maret 2025

Section

Articles

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Copyright (c) 2025 Ridho Sholehurrohman, Ivani Valentine, Kurnia Muludi

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