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All Journal Indonesian Journal of Artificial Intelligence and Data Mining
Galea, Nanda
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Computer Science & IT

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Comparative Evaluation of Optuna-Optimized Radial Basis Function and Sigmoid Kernels in Support Vector Machine for Smart Air Quality Classification Galea, Nanda; Rahman, A; Maulidda, Renny; Husni, Nyayu Latifah
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.37563

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.
Co-Authors A Rahman Nyayu Latifah Husni, Nyayu Latifah Renny Maulidda, Renny