Design of Defect Classification on Clay Tiles using Support Vector Machine (SVM)

The current industry has been developing rapidly which make the company must have high competitiveness by maintaining the product's quality and quantity produced by the company. PT. XYZ is one of the companies in the clay tile industry. There are classifications of PT. XYZ products: good tile, white stone tile, and cracked tile in quality control. During its classification, PT XYZ still uses the traditional method of vision. The traditional detection of errors or defects only using human vision can slow down the process and increase the error rate. With the rapid development of automation can overcome this by the discovery of artificial visual detectors that use measurement methods, image preprocessing, and algorithms in detecting these defects. In this study using the Support Vector Machine (SVM) method for classifying defects and Local Binary Pattern (LBP) method for extracting the feature on tiles. Direct image taking in this study use raspberry pi and create the algorithm system using python software. The results of this study concluded that the highest level of accuracy was 87.5% using linear kernels. While the required time for direct classification is 10.63 seconds.