IMPLEMENTASI MODEL KLASIFIKASI KEMATANGAN PISANG MENGGUNAKAN ARSITEKTUR CNN INCEPTION V3 DAN VGG16 - Sephia Mulyawati; Miftah Andriansyah (CV SWA Anugerah, 2025)
Banana ripeness level is a crucial factor in determining the quality and freshness of agricultural products. This research aims to develop a Convolutional Neural Network (CNN) model to classify the ripeness levels of bananas—from unripe to overripe—with the goal of assisting farmers in the sorting process more consistently and efficiently. Two CNN architectures, InceptionV3 and VGG16, were utilized to evaluate their respective performances. This research employed the CRISP-DM methodology, which consists of business understanding, data understanding, data preparation, modeling, and evaluation. The dataset used was obtained from Dataverse Telkom University and consists of four classes: unripe, semi-ripe, ripe, and overripe. The results showed that the VGG16 model achieved the highest accuracy of 92%, while InceptionV3 achieved an accuracy of 87%. However, both models struggled with classifying the