Smart Fire Safety: Analyzing Radial Basis Function Kernel in SVM for IoT-driven Smoke Detection - Ordiyasa, I Wayan; Diqi, Mohammad; Lustiyati, Elisabeth Deta; Hiswati, Marselina Endah; Salsabela, Marcella (Informatics Engineering Department of Halu Oleo University, 2024)
This research explores the application of Support Vector Machine (SVM) with the Radial Basis Function (RBF) kernel in smoke detection using a dataset collected from Internet of Things (IoT) devices, specifically Photoelectric Smoke Detectors. With 62,630 records and 16 attributes, the study aims to address limitations in smoke detection technology that may impact system accuracy. Through RBF kernel analysis, the SVM model demonstrates the capability to recognize complex patterns related to smoke presence, achieving an accuracy rate of 96.85%. The Classification Report reveals high precision, recall, and f1-score for both