<![CDATA[Illegal logging is a significant environmental issue in Indonesia, particularly in the tropical forests of Southeast Sulawesi, which threatens biodiversity and contributes to global climate change. Manual monitoring of illegal activities in remote areas is often ineffective, necessitating innovative and real-time solutions for early detection. This study aims to develop an early detection system to distinguish the sound of chainsaws commonly used in illegal logging activities from other machine sounds such as RX King motorcycles and ketinting boats. The KY-038 sound sensor connected to an Arduino was used to capture environmental sounds, and the obtained data was classified using the K-Nearest Neighbors (KNN) algorithm. Experiments were conducted by collecting training data and testing the system with sound samples from each machine. The results showed that the developed sound detection system could classify the sounds of chainsaws, RX King motorcycles, and ketinting boats with good performance. With the optimal k value in KNN, the average classification accuracy reached 90%. This system can be used as an effective monitoring tool for the early detection of illegal logging activities, contributing to the conservation of tropical forests.]]>
Copyrights © 2025
