Deteksi Jamur Beracun dengan Algoritma Convolutional Neural Network dan Arsitektur EfficientNet-B0

 (*)Muhammad Wildan Mauludy Mail (Universitas Budi Luhur, Jakarta Selatan, Indonesia)
 Devita Rulyana (Universitas Budi Luhur, Jakarta Selatan, Indonesia)
 Mardi Hardjianto (Universitas Budi Luhur, Jakarta Selatan, Indonesia)

(*) Corresponding Author

Submitted: January 4, 2024; Published: January 30, 2024

Abstract

Indonesia is a tropical country that has abundant natural resources and biodiversity, one of which is mushrooms. Mushrooms have various shapes and types. Some of them contain mushrooms that cannot be consumed because they contain toxins that will have an impact on human health. Mushroom species that can be consumed sometimes have a similar shape to mushrooms that cannot be consumed, causing cases of poisoning due to consuming the wrong mushrooms. This research focuses on detecting poisonous mushrooms using a Convolutional Neural Network (CNN) with the EfficientNet-B0 architecture. Mushroom data was obtained from Kaggle, and after praprocessing, the model was trained by varying the number of epochs and batch size. Based on the results of research and discussion on the detection of poisonous and non-toxic mushrooms, it is concluded that the CNN algorithm with the EfficientNet-B0 architecture can differentiate between poisonous and non-toxic mushrooms with a high level of accuracy. In scenario testing, the model trained using batch size 32 had an accuracy of 84.2% and loss of 0.39, precision of 0.855, recall of 0.805, and f1 score of 0.815. This shows that the CNN architecture EfficientNet-B0 is an efficient and accurate approach in classifying poisonous and non-poisonous mushrooms. Apart from that, this research also found that parameters such as the number of epochs and the number of batch sizes influence the model training process.

Keywords


Classification; Mushroom; CNN; EfficientNet-B0; Digital Image Processing

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