Perbandiangan VGG16 dan MobileNetV2 untuk Klasifikasi Tingkat Kematangan Buah Apel - Kurniadi, Ahmad Zulfi; Wahyuni, Ida; Ivani, Aryani Rizky Rahmalia; Andira, Aysza Belia Auly (Institut Tekonologi dan Binisi Asia Malang, 2025)
This research discusses deep learning to classify the maturity level of apples, especially in Batu City, which is known as the center of apple production in Indonesia. This study implements the Visual Geometry Group16 (VGG) model as a deep learning-based image classification method to accurately identify the maturity level of apples. The data used was in the form of images of apples with various levels of ripeness, which were categorized into ripe, half-ripe, and raw. The design of the VGG16 model was chosen because of its simple yet powerful architecture in the extraction of visual features. This research process includes collecting apple image data, preprocessing, model training, and performance evaluation based on accuracy, precision, recall, and F1-score metrics. The results of the experiment showed that the accuracy obtained was 98%. So the VGG16 model is able to classify the maturity level of apples, with the potential for application in automation systems in the agricultural sector.