information-09-00005-v4.pdf - information Article A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite

information-09-00005-v4.pdf - information Article A...

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information Article A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection Muhammad Achirul Nanda 1 ID , Kudang Boro Seminar 1, * ID , Dodi Nandika 2 and Akhiruddin Maddu 3 1 Faculty of Agricultural Technology, Bogor Agricultural University, Bogor 16680, West Java, Indonesia; [email protected] 2 Faculty of Forestry, Bogor Agricultural University, Bogor 16680, West Java, Indonesia; [email protected] 3 Faculty of Mathematics and Natural Sciences, Bogor Agricultural University, Bogor 16680, West Java, Indonesia; [email protected] * Correspondence: [email protected]; Tel.: +62-8164834625 Received: 10 December 2017; Accepted: 28 December 2017; Published: 2 January 2018 Abstract: Termites are the most destructive pests and their attacks significantly impact the quality of wooden buildings. Due to their cryptic behavior, it is rarely apparent from visual observation that a termite infestation is active and that wood damage is occurring. Based on the phenomenon of acoustic signals generated by termites when attacking wood, we proposed a practical framework to detect termites nondestructively, i.e., by using the acoustic signals extraction. This method has the pros to maintain the quality of wood products and prevent higher termite attacks. In this work, we inserted 220 subterranean termites into a pine wood for feeding activity and monitored its acoustic signal. The two acoustic features (i.e., energy and entropy) derived from the time domain were used for this study’s analysis. Furthermore, the support vector machine (SVM) algorithm with different kernel functions (i.e., linear, radial basis function, sigmoid and polynomial) were employed to recognize the termites’ acoustic signal. In addition, the area under a receiver operating characteristic curve (AUC) was also adopted to analyze and improve the performance results. Based on the numerical analysis, the SVM with polynomial kernel function achieves the best classification accuracy of 0.9188. Keywords: acoustic signal; kernel function; support vector machine; termite detection 1. Introduction Living in large underground colonies, termites can attack any wood that has a direct contact to the ground and can even lead to the death of a healthy tree. Termites are harmful pests that economically impact the quality of the wood in wooden buildings, forest trees and crops. As can be seen in Figure 1 , it shows the initial attack of subterranean termites on Acacia crassicarpa plantation, Riau Province, Indonesia. In addition, the damage of wooden buildings by termites is also easy to find in Bogor city and surrounding areas [ 1 ]. In fact, some areas of the important buildings in Indonesia have been seriously attacked e.g., Presidential Palace, Istana Merdeka, Jakarta, etc. [ 1 ]. Nandika et al. [ 2 ] reported that the cost, due to termite attacks to wooden buildings, was estimated to reach about Rp 8.7 trillion in 2015 not including treatment costs, repairs of the damaged buildings and loss of property value.
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