<![CDATA[Identification of coffee types after roasting is a major challenge because visual changes make the appearance of coffee beans diverse. Subjective assessment methods are time-consuming, so digital image processing and CNN techniques show potential to solve complex classification problems. This study develops a ResNet50-based CNN model to identify four types of coffee beans (Robusta, Arabica, Excelsa, and Liberica) after roasting and analyzes the effectiveness of pre-processing and augmentation techniques in improving classification performance. The research employed quantitative methodology with three phases: data collection, pre-processing with augmentation, and CNN implementation. The dataset consisted of 2,000 coffee bean images, with 500 images for each class: Arabica, Excelsa, Liberica, and Robusta, ensuring balanced representation across all coffee varieties from a local Indonesian coffee supplier, using smartphone. Preprocessing included normalization and resizing, while augmentation comprised various image transformation techniques. Model performance was evaluated using performance metrics. Results showed an overall accuracy of 94.50%, with Liberica demonstrating exceptional performance (100% precision, 98% recall). Robusta achieved 97% precision and 98% recall, while Arabica showed 86.5% precision with 96% recall. Excelsa achieved 95.6% precision and 86% recall. The model successfully classified 378 out of 400 test samples, with Excelsa representing the primary classification challenge due to visual similarity with other varieties post-roasting. Analysis of misclassifications revealed improved distinction between coffee varieties, with the model demonstrating strong generalization capabilities across all classes. The ResNet50 model successfully identified coffee beans with good accuracy but experienced difficulty distinguishing varieties with similar visual characteristics. Future work should explore improved methods and larger datasets for accuracy.]]>
