Implementasi Deep Learning Untuk Identifikasi Jenis Biji Kopi Menggunakan Metode Convolutional Neural Network - Pratama, Munawwar Anugrah; Hadiwandra, T. Yudi (CSRID (Computer Science Research and Its Development Journal), 2025)
Indonesia is one of the largest coffee producers in the world, with various types of coffee beans such as Arabica, Robusta, and Liberica. Each type of coffee bean has unique characteristics that influence the taste, aroma, and overall quality of the coffee. However, many people are still unable to visually distinguish between these types of beans. This research aims to develop a Deep Learning-based system using the Convolutional Neural Network (CNN) method with the Xception architecture to identify coffee bean types from images. The dataset was obtained from direct image collection and online sources, then processed through preprocessing and data augmentation stages. The model training process was conducted using transfer learning techniques to improve classification performance. The resulting model is capable of classifying coffee bean images into three main categories with an accuracy 81.63%. The system is implemented as a web interface using Flask, allowing users to upload images of coffee beans and obtain classification results via a website. This study demonstrates that the CNN method with Xception architecture is effective for visual recognition of coffee bean types and can be a solution to help the general public in identifying different coffee bean varieties. This study aims to develop a deep learning–based system using the Convolutional Neural Network (CNN) method with the Xception architecture to identify coffee bean types from images. A total of 600 images of Arabica, Robusta, and Liberica beans were collected from primary and online sources, and then divided into training (80%), validation (10%), and testing (10%) sets. The dataset was processed through image preprocessing and augmentation techniques such as rotation, flipping, zooming, and brightness adjustment to improve model generalization. The training was performed using a transfer learning approach, followed by fine-tuning several deeper layers to enhance feature extraction. Evaluation was conducted using a confusion matrix and F1-score to validate class-wise performance. The model achieved an accuracy of 81.63% using the testing dataset. In practical implementation through a Flask-based website, the system achieved above 90% accuracy for several input angles, indicating strong recognition ability under controlled image conditions. This work demonstrates that the CNN Xception model is effective for visual identification of coffee bean types and can be applied as a practical solution to assist the general public, farmers, and coffee industry practitioners. Future enhancement may include expanding bean classes, optimizing architecture, and real-world testing.