Comparative Evaluation of Optuna-Optimized Radial Basis Function and Sigmoid Kernels in Support Vector Machine for Smart Air Quality Classification

Nanda Galea; A Rahman; Renny Maulidda; Nyayu Latifah Husni

doi:10.24014/ijaidm.v8i3.37563

Comparative Evaluation of Optuna-Optimized Radial Basis Function and Sigmoid Kernels in Support Vector Machine for Smart Air Quality Classification

Nanda Galea, A Rahman, Renny Maulidda, Nyayu Latifah Husni

Abstract

Poor air quality can have a serious impact on human health, so a classification system capable of accurately identifying air conditions is needed. This research proposes an air quality classification method using the Support Vector Machine (SVM) algorithm with two types of non-linear kernels, namely Radial Basis Function (RBF) and Sigmoid. The data used is obtained from various environmental sensors that record parameters such as CO, smoke, HC, TVOC, eCO₂, temperature, and humidity, and then collected in the form of historical datasets. To enhance the accuracy and efficiency of the model, hyperparameter optimization was performed automatically using Optuna. The evaluation results showed that SVM with RBF kernel performed better than Sigmoid kernel, achieving an accuracy value of 96.67% and F1-score of 96.80%. In addition, RBF also showed higher stability in 5-fold cross validation. This research shows that the combination of SVM and Optuna is effective in building an accurate air quality classification system, and has the potential to be further developed as a sensor based in air monitoring system and IoT.

Keywords

Air Quality Classification; Optuna; Radial Basis Function; Sigmoid; Support Vector Machine

References

D. Hidajat, F. G. Tilana, and I. G. B. S. A. Kusuma, “Dampak Polusi Udara terhadap Kesehatan Kulit,” Unram Medical Journal, vol. 12, no. 4, pp. 371–378, Dec. 2023, doi: 10.29303/jk.v12i4.4565.

P. Yin et al., “The effect of air pollution on deaths, disease burden, and life expectancy across China and its provinces, 1990–2017: an analysis for the Global Burden of Disease Study 2017,” Lancet Planet Health, vol. 4, no. 9, pp. e386–e398, Sep. 2020, doi: 10.1016/S2542-5196(20)30161-3.

G. Syuhada et al., “Impacts of Air Pollution on Health and Cost of Illness in Jakarta, Indonesia,” Int J Environ Res Public Health, vol. 20, no. 4, Feb. 2023, doi: 10.3390/ijerph20042916.

O. Farooq et al., “An enhanced approach for predicting air pollution using quantum support vector machine,” Sci Rep, vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-69663-2.

J. Cervantes, F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez, “A comprehensive survey on support vector machine classification: Applications, challenges and trends,” Neurocomputing, vol. 408, pp. 189–215, Sep. 2020, doi: 10.1016/j.neucom.2019.10.118.

C. Cortes and V. Vapnik, “Support-Vector Networks,” Machine Leaming, vol. 20, pp. 273–297, 1995, doi: https://doi.org/10.1007/BF00994018.

M. Jabardi, “Support Vector Machines: Theory, Algorithms, and Applications,” Infocommunications Journal, vol. 17, no. 1, pp. 66–75, Mar. 2025, doi: 10.36244/ICJ.2025.1.8.

K. L. Du, B. Jiang, J. Lu, J. Hua, and M. N. S. Swamy, “Exploring Kernel Machines and Support Vector Machines: Principles, Techniques, and Future Directions,” Mathematics, vol. 12, no. 24, Dec. 2024, doi: 10.3390/math12243935.

M. Noguer i Alonso, “The Mathematics of Support Vector Machines,” Oct. 2024.

I. Nur Hermawan and A. Rinaldi Dikananda, “Comparing optimization hyperparameter long short term memory for rainfall prediction model,” Institute of Computer Science (IOCS), vol. 16, no. 6, pp. 405–414, Jan. 2025.

Y.-J. Chang, Y.-L. Lin, and P.-F. Pai, “Support Vector Machines with Hyperparameter Optimization Frameworks for Classifying Mobile Phone Prices in Multi-Class,” Electronics (Basel), vol. 14, no. 11, p. 2173, May 2025, doi: 10.3390/electronics14112173.

T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama, “Optuna: A Next-generation Hyperparameter Optimization Framework,” in Preprint, Jul. 2019. [Online]. Available: http://arxiv.org/abs/1907.10902

M. Conciatori, A. Valletta, and A. Segalini, “Improving the quality evaluation process of machine learning algorithms applied to landslide time series analysis,” Comput Geosci, vol. 184, Feb. 2024, doi: 10.1016/j.cageo.2024.105531.

O. Rainio, J. Teuho, and R. Klén, “Evaluation metrics and statistical tests for machine learning,” Sci Rep, vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-56706-x.

A. S. Handayani, S. Soim, T. E. Agusdi, and N. L. Husni, “Air Quality Classification Using Support Vector Machine,” Computer Engineering and Applications, vol. 10, no. 1, 2021.

L. Widyarini and H. D. Purnomo, “Air Quality Prediction Using the Support Vector Machine Algorithm,” Journal of Information Systems and Informatics, vol. 6, no. 2, pp. 652–661, Jun. 2024, doi: 10.51519/journalisi.v6i2.705.

A. Nandakumar, R. P. Scholar, and A. Professor, “Classification Of Air Pollution Levels Using Supervised Machine Learning Algorithm,” Internatinal Journal of Creative Research Thoughts (IJRT), vol. 10, no. 4, pp. 2320–2882, Apr. 2022, [Online]. Available: www.ijcrt.org

N. L. Husni, S. Nurmaini, I. Yani, and A. Silvia, “Intelligent Sensing Using Metal Oxide Semiconductor Based-on Support Vector Machine for Odor Classification,” International Journal of Electrical and Computer Engineering (IJECE), vol. 8, pp. 4133–4147, Dec. 2018, doi: 10.11591/ijece.v8i6.pp4133-4147.

M. N. Anshory, M. I. Mazdadi, T. H. Saragih, I. Budiman, and S. W. Saputro, “Application of Adaboost Algorithm with SMOTE and Optuna Techniques in Sleep Disorder Classification,” Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 7, no. 2, pp. 415–426, 2025, doi: 10.35882/ijeeemi.v6i3.407.

L. H. Lai et al., “The Use of Machine Learning Models with Optuna in Disease Prediction,” Electronics (Switzerland), vol. 13, no. 23, Dec. 2024, doi: 10.3390/electronics13234775.

V. Sinap, “Improving Machine Failure Prediction with Grey Wolf, Whale Optimization, and Optuna Techniques,” Gazi University Journal of Science Part A: Engineering and Innovation, vol. 12, no. 1, pp. 154–174, Mar. 2025, doi: 10.54287/gujsa.1544942.

A. Ainul Yaqin, M. Agung Barata, and N. Mahmudah, “Implementation of the Random Forest Algorithm with Optuna Optimization in Lung Cancer Classification,” Sistemasi: Jurnal Sistem Informasi, vol. 14, no. 2, pp. 561–569, 2025, [Online]. Available: http://sistemasi.ftik.unisi.ac.id

C. M. Rosca, M. Carbureanu, and A. Stancu, “Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas,” Applied Sciences (Switzerland), vol. 15, no. 8, Apr. 2025, doi: 10.3390/app15084390.

İ. Aksangür, B. Eren, and C. Erden, “Evaluation of data preprocessing and feature selection process for prediction of hourly PM10 concentration using long short-term memory models,” Environmental Pollution, vol. 311, Oct. 2022, doi: 10.1016/j.envpol.2022.119973.

E. Antony, N. S. Sreekanth, R. K. Sunil Kumar, and T. Nishanth, “Data preprocessing techniques for handling time series data for environmental science studies,” International Journal of Engineering Trends and Technology, vol. 69, no. 5, pp. 196–207, May 2021, doi: 10.14445/22315381/IJETT-V69I5P227.

D. NURWAHA, “Comparison of kernel functions of support vector machines: A case study for the solar cell output power prediction,” International Journal of Energy Applications and Technologies, vol. 7, no. 1, pp. 1–6, Apr. 2020, doi: 10.31593/ijeat.630789.

T. Kee and W. K. O. Ho, “Optimizing Machine Learning Models for Urban Sciences: A Comparative Analysis of Hyperparameter Tuning Methods,” Preprints (Basel), Jul. 2024, doi: 10.20944/preprints202406.0264.v2.

M. Jonas Simanullang, R. Rosnelly, and B. Subhan Riza, “Classification of Big Data Stunting in North Sumatra Using Support Vector Regression Method 1,” Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM), vol. 8, no. 1, pp. 1–10, 2025, doi: 10.24014/ijaidm.v8i1.32177.

A. Tikaningsih, P. Lestari, A. Nurhopipah, I. Tahyudin, E. Winarto, and N. Hassa, “Telematika Optuna Based Hyperparameter Tuning for Improving the Performance Prediction Mortality and Hospital Length of Stay for Stroke Patients,” Telematika, vol. 17, no. 1, pp. 1–16, 2024, doi: 10.35671/telematika.v17i1.2816.

R. Sholehurrohman, I. Valentine, and K. Muludi, “Penilaian Kualitas Udara dan Polusi Menggunakan Algoritma Support Vector Machine,” Jurnal SAINTEKOM, vol. 15, no. 1, pp. 56–68, Mar. 2025, doi: 10.33020/saintekom.v15i1.810.

A. Ainul Yaqin et al., “Sistemasi: Jurnal Sistem Informasi Implementation of the Random Forest Algorithm with Optuna Optimization in Lung Cancer Classification,” Sistemasi, vol. 14, no. 2, pp. 561–569, 2025, [Online]. Available: http://sistemasi.ftik.unisi.ac.id

M. Gian, Y. Nita, and A. Solichin, “Early Detection of Hepatitis Disease Using Machine Learning Algorithms,” Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM), vol. 8, no. 3, pp. 545–555, 2025, doi: 10.24014/ijaidm.v8i3.38084.

G. Arisona, A. P. Taruna, D. Irwanto, A. B. Bestari, and W. Juniawan, “Classification Based on the Support Vector Machine for Determining Operational Targets for Controlling Electricity Usage with Conventional Meters: A Case Study of Industrial and Business Tariff Customers from PT PLN (Persero) Indonesia,” IEEE Access, 2025, doi: 10.1109/ACCESS.2025.3529295.

K. B. Dasari and N. Devarakonda, “Detection of TCP-Based DDoS Attacks with SVM Classification with Different Kernel Functions Using Common Uncorrelated Feature Subsets,” International Journal of Safety and Security Engineering, vol. 12, no. 2, pp. 239–249, Apr. 2022, doi: 10.18280/ijsse.120213.

N. Alinda Rahmi and S. Defit, “The Use of Hyperparameter Tuning in Model Classification: A Scientific Work Area Identification,” International Journal on Informatics Visualization, vol. 8, no. 4, pp. 2181–2188, 2024, [Online]. Available: www.joiv.org/index.php/joiv

DOI: http://dx.doi.org/10.24014/ijaidm.v8i3.37563

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