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Scirj, Volume VI [2019]
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Scirj Volume VII, Issue IV, April 2019 Edition
ISSN: 2201-2796

Defect Detection of Ceramic Tiles using Median Filtering, Morphological Techniques, Gray Level Co-occurrence Matrix and K-Nearest Neighbor Method

Riza Alamsyah, Ade Davy Wiranata, Rafie

Abstract: Manufacturing industry companies must be able to maintain the quality of each product produced, including manufacturing companies that produce ceramic tiles. For several years, automatic visual inspection has been applied to determine the quality of ceramic tiles produced. The difficulty of detecting defective ceramic tiles can have an impact on decreasing the quality of production, decreasing the level of consumer confidence, and decreasing profits for the company. The problem discussed in this research is how to defect detection of ceramic tiles so that the model built can improve accuracy to defect detection of ceramic tiles. The solution to this problem is to collect data in the form of ceramic tiles images, then preprocessing images data using Median Filtering to eliminate salt and paper noise and Morphological Techniques to improve images segmentation results. After preprocessing, texture image extraction data is based on texture using the Gray Level Co-occurrence Matrix (GLCM) method which is continued by classifying images data using the K-Nearest Neighbor (KNN) method. The results of this research are models that are built using the Median Filtering, Morphological Techniques, Gray Level Co-occurrence Matrix and K-Nearest Neighbor method can improve accuracy to defect detection of ceramic tiles with an accuracy value of 98.9474% for k=3.

Reference this Paper: Defect Detection of Ceramic Tiles using Median Filtering, Morphological Techniques, Gray Level Co-occurrence Matrix and K-Nearest Neighbor Method by Riza Alamsyah, Ade Davy Wiranata, Rafie published at: "Scientific Research Journal (Scirj), Volume VII, Issue IV, April 2019 Edition, Page 41-45 ".

Search Terms: Digital Image Processing¸ Median Filtering, Morphological Techniques, GLCM dan KNN

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