Classification of Coconut Fruit Ripeness Level Using Convolutional Neural Network (CNN) Method - Muhammad Rizki; Rudi Heriansyah; Dwi Verano (Jurnal Komputer, Informasi dan Teknologi, 2026)
Manual assessment of coconut ripeness is often subjective and causes post-harvest losses of up to 25% in Indonesia, the world's largest coconut producer. This study aims to develop a CNN VGG-19 model for automatic classification of three ripeness levels (immature, medium, mature) with accuracy >95%. The quantitative experimental method uses supervised learning with a dataset of 900 original images (300/class) from local plantations in South Sumatra, augmented to 3000 images. Instruments include Python/TensorFlow on Google Colab, preprocessing (rembg background removal, resizing 224x224), training 10 epochs of the Adam optimizer. Analysis uses a confusion matrix, accuracy, precision, recall, and F1-score. The results show a progressive accuracy from 14% (40 test data/class) to 98% (200 test data/class). Conclusion: VGG-19 transfer learning with data augmentation is effective for local coconut ripeness classification, potentially integrating into mobile applications for the processing industry.