AbstrakPerusahaan industri manufaktur harus dapat menjaga kualitas dari setiap produk yang diproduksi, termasuk perusahaan industri manufaktur yang memproduksi ubin keramik. Selama beberapa tahun, inspeksi visual secara otomatis sudah diterapkan untuk menentukan kualitas ubin keramik yang diproduksi. Sulitnya mendeteksi ubin keramik yang cacat bisa berdampak pada menurunnya kualitas hasil produksi, menurunnya tingkat kepercayaan konsumen, dan penurunan laba bagi perusahaan. Masalah yang dibahas di dalam penelitian ini adalah bagaimana mendeteksi ubin keramik yang cacat sehingga model yang dibangun dapat meningkatkan akurasi untuk mendeteksi ubin keramik yang cacat. Langkah penyelesaian masalah ini adalah dengan mengumpulkan data berupa citra dari ubin keramik, kemudian data citra dilakukan preprocessing menggunakan Median Filtering untuk menghilangkan noise salt and paper dan Teknik Morfologi untuk memperbaiki hasil segmentasi citra. Setelah dilakukan preprocessing, data citra diekstraksi ciri berdasarkan tekstur dengan menggunakan metode Gray Level Co-occurrence Matrix (GLCM) yang dilanjutkan dengan mengklasifikasikan data citra menggunakan metode K-Nearest Neighbor (KNN). Hasil dari penelitian ini adalah model yang dibangun menggunakan metode K-Nearest Neighbor dapat meningkatkan akurasi untuk mendeteksi kecacatan pada ubin keramik dengan nilai akurasi sebesar 98.9474% untuk k = 3. Kata kunci-Digital Image Processing¸ Median Filtering, Teknik Morfologi, GLCM dan KNN AbstractManufacturing 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 K-Nearest Neighbor method can improve accuracy to defect detection of ceramic tiles with an accuracy value of 98.9474% for k=3.

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 Cooccurrence 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 Cooccurrence 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. Keywords-Digital Image Processing¸ Median Filtering, Morphological Techniques, GLCM dan KNN. I.

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