Treffer: The hybrid design of supervised learning algorithm for design and development in classifications a defect in clay tiles.

The strength of the company's competitiveness is needed because the current industrial development is very rapid. This is necessary with the aim of maintaining the quality and quantity of the products produced according to company standards. One of the companies that must maintain the quality and quantity is PT. XYZ is a clay tile company. The classification of products used by this company to maintain good quality is three classes, namely good tile, white stone tile, cracked tile. However, the practice of quality control based on classification still uses the traditional way by relying on sight. It can increase errors and slow down the process. This can be overcome with artificial visual detectors. This is a result of the rapid development of automation. So to detect defects can use image preprocessing, supervised learning algorithms, and measurement methods. Support Vector Machine (SVM) is used in this study to perform classification while feature extraction on clay tiles used the Local Binary Pattern (LBP) method. The algorithm is made using python while for image retrieval the raspberry pi is used. The linear kernel on the SVM algorithm is used in this study. The conclusion in this study obtained 86.95% the highest accuracy with a linear kernel. It takes 10.625 seconds to classify. [ABSTRACT FROM AUTHOR]

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