<![CDATA[Fire disasters remain a serious and recurring problem in Indonesia, particularly in densely populated urban areas and forest and land regions, causing significant material losses and posing serious threats to human safety. Early fire detection is therefore essential to minimize damage and casualties. Conventional fire detection systems suffer from limited coverage and delayed response, motivating the use of image-based fire detection as an alternative solution. Among image processing approaches, thresholding-based methods are widely used due to their simplicity and low computational cost. However, their performance may vary under different environmental conditions. To improve robustness, thresholding techniques are often combined with machine learning classifiers such as Support Vector Machines (SVM). This study presents a comparative analysis of fire image classification using standalone thresholding and thresholding combined with SVM. The dataset consisted of 999 fire and non-fire images collected from publicly available sources and was divided into training and testing sets using an 80:20 split. Experimental results show that the standalone thresholding method achieves an accuracy of 87.39%, outperforming the thresholding combined with SVM, which achieves an accuracy of 75.58%. Although the SVM classifier successfully identifies all fire images, it fails to distinguish non-fire images, resulting in a high false positive rate. These findings indicate that increased model complexity does not necessarily improve classification performance when feature representation is limited. These results provide practical insights into the effectiveness of lightweight fire image classification methods and highlight the importance of feature selection in machine learning-based fire detection systems.]]>
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