Comparison Analysis of SVM and KNN Algorithm For IoT-Based Home Fire Detection System

Abstract

Internet of Things (IoT) is a network that connects various integrated objects. One application of IoT is a fire detection system to provide remote warnings. In this study, IoT deployments were performed using SVM (Support Vector Machine) algorithm and KNN (K-Nearest Neighbor) algorithm. The algorithm is attached to the ESP32 microcontroller for data classification. The sensors used include temperature, humidity, fire, and smoke sensors. In case of fire a warning will be sent to Telegram. Classification results were tested with Quality of Service (QoS) parameters on throughput, delay, and jitter values, as well as with the confusion matrix with 3 simulation variations. The test outcomes display that the system is in the correct category with an average throughput value of 1.848 bps and the best value of 1.858 bps, an average delay of 593.045 ms, and a jitter of 594.188 ms. The highest accuracy was obtained in simulation 2, namely 100% for SVM and 97.5% for KNN with K=1 in KNN. Meanwhile, in simulation 1 KNN has an accuracy of 95% and SVM 98%, simulation 3 KNN 97% and SVM 100%. Thus, the SVM algorithm can classify the system better than the KNN algorithm.