Klasifikasi Jenis Jamur Edible Menggunakan Convolutional Neural Network: Studi Kasus pada Jamur Tiram, Enoki, dan Truffle

Abstract

This study uses a Convolutional Neural Network (CNN) to develop a mushroom type classification model that can differentiate between truffles, enoki and oyster mushrooms very accurately. The dataset consisting of mushroom images is collected from various sources and processed through data augmentation techniques such as rotation, zoom, flip, and shifting to improve model generalization. For final classification, the CNN model used consists of several convolution and pooling layers, followed by a dense layer. The study results show that this model has a validation accuracy of 79.31% after twenty training epochs. Although there were some misclassifications between oyster and enoki mushrooms, data augmentation techniques were shown to improve model performance. This research shows that CNN can be used effectively to classify various types of fungi. This also shows that CNN has many potential applications in the agricultural and food industries. Increasing datasets, studying more complex model architectures, and using advanced data augmentation methods such as Generative Adversarial Networks (GANs) are some suggestions for further research.