<![CDATA[Manufacturing industry has become one of the largest sectors in Indonesia, driven by increasing demand from the public. A primary concern to meet both local and international market needs is product quality. In ensuring high-quality standards, production processes require strict quality control. One common issue in quality control is defects occurring during the welding process, which significantly affects inspection cycle times. To address this, the Convolutional Neural Network (CNN) approach with VGG-16 architecture can help classify defects in the welding process. This method not only expedites the defect classification process but also enhances the accuracy of identifying product defects. The stages of implementing this method include dataset preparation, data preprocessing, CNN model design, model training, and performance evaluation. Evaluation results demonstrate that the use of automatic defect detection technology, especially with balanced data scenarios, can significantly improve quality control performance. Accuracy, precision, recall, and F1-score achieve excellent levels, reaching 92%. Thus, this research provides a significant contribution to enhancing production efficiency and improving product quality in the motorcycle manufacturing industry in Indonesia. It is hoped that the use of this technology will assist manufacturing companies in identifying and addressing production defects more effectively, thereby enhancing the overall competitiveness of Indonesia's manufacturing industry.]]>
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