Implementasi Algoritma CNN dalam Identifikasi Infeksi Jamur Superfisialis
DOI:
https://doi.org/10.55606/teknik.v3i3.2539Keywords:
Fungal Infections, Convolutional Neural Network, MobileNetV3Abstract
Mushrooms, as eukaryotic organisms that dominate the kingdom Fungi, play a crucial role as decomposers of organic matter in ecosystems. The diversity of mushrooms encompasses a variety of species, ranging from microscopic to macroscopic, widely distributed in various environments. Superficial fungal infections, or superficial mycoses, focus on fungal infections limited to the surface of the skin, nails, or hair. Such infections are typically localized, not penetrating deep into tissues and remain primarily a local condition. In the context of this study, the optimal model developed through training successfully achieved a validation accuracy of 92.590% and a training accuracy of 88.933%. Evaluation results indicate a precision value of 94%, a recall value of 93%, and an F1-score value of 92%. These findings highlight the success of the model in classifying and evaluating superficial fungal infections with high precision.
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