Classification of Melinjo Fruit Ripeness Using a Convolutional Neural Network (CNN) Based on Digital Images - Kurniawan, Anggi Ade; Mutiara Sari, Nur Latifah Dwi; Wibowo, Setyoningsih (Politeknik Negeri Batam, 2026)
The subjective and ineffective manual sorting of melinjo fruit, a key ingredient in Indonesian cuisine, results in inconsistent quality. This study aims to create and evaluate an automated classification system for judging the ripeness of Gnetum gnemon fruit in order to solve these issues and offer a reliable and objective quality control method. The approach was to create a customized Deep Convolutional Neural Network (Deep-CNN). The model was trained and evaluated using a simple dataset of 5,718 images that were separated into three maturity levels: raw, semi-ripe, and fully ripe. Twenty percent of the dataset was used for testing, and the remaining 80 percent was used for training. Image preparation techniques like contrast enhancement and scaling to 250x250 pixels were applied in order to optimize the model's input data. The evaluation was conducted using a test dataset consisting of 1,144 photos. After eight epochs of training with the Adam optimizer, the generated Deep-CNN model demonstrated remarkable efficacy with a final classification accuracy of 99.91%. The high level of performance that remained throughout the testing phase confirmed the model's strong ability to accurately identify the ripeness levels of melinjo fruit. The previously unresolved issue of automated melinjo classification is addressed in this work with a tailored and remarkably accurate (99.91%) solution. Its primary advantage is that it provides a trustworthy and unbiased technical alternative to subjective hand sorting. This directly meets industry needs by offering a scalable method to improve operational effectiveness, standardize product quality, and increase the commercial value of melinjo fruit of agricultural products.