Comparative Analysis of Naive Bayes and Fuzzy Logic Algorithm in Fire Classification System - Ria Rismayati; Suriyati Suriyati; Parama Diptya Widayaka (Universitas Islam Indragiri, 2026)
Building fires can cause losses in several areas, including property damage, environmental pollution, loss of life, injury, and psychological trauma. Building fires can occur due to several factors such as gas leaks, short circuits, overheating electronic devices, the presence of flammable materials, and human error. In fire mitigation efforts, devices are generally used as early warnings, but their implementation is often less than optimal due to system malfunctions. Therefore, this study aims to develop an early warning system that can detect potential fires before they spread. The methods used in this study are the naïve Bayes and fuzzy logic methods, which then compare each method to determine the most effective method. The results of this study indicate that the naïve Bayes and fuzzy logic methods have successfully classified potential fires well. From 30 experimental data, the naïve Bayes algorithm produced an accuracy of 96%, while the fuzzy logic algorithm produced an accuracy of 100%. The naïve Bayes algorithm shows reliable performance in classifying extreme data while the fuzzy algorithm can detect the ‘Danger’ status even though not all parameters are in a dangerous condition.