Analisis Perbandingan Arsitektur EfficientNetV2B0 dan Xception pada Klasifikasi Tumbuhan Obat Berdasarkan Citra Daun
DOI:
https://doi.org/10.26418/justin.v14i2.95980Keywords:
Image Classification, Medicinal Plants, Deep Learning, EfficientNetV2B0, XceptionAbstract
Indonesia memiliki keanekaragaman tumbuhan obat yang tinggi, namun proses klasifikasinya masih sering dilakukan secara manual dan kurang efisien, terutama karena kemiripan bentuk daun antar tumbuhan. Untuk mengatasi hal ini, pendekatan klasifikasi berbasis deep learning dapat dilakukan sebagai solusi yang lebih akurat dan otomatis. Penelitian ini membandingkan performa dua arsitektur deep learning, yaitu EfficientNetV2B0 dan Xception, dalam mengklasifikasikan tumbuhan obat berdasarkan citra daun. Dataset yang digunakan terdiri dari 3000 citra daun tunggal dari 10 jenis tumbuhan obat, yang gambarnya diperoleh melalui pengambilan secara langsung. Tahapan penelitian meliputi pengumpulan data, preprocessing data, training model menggunakan transfer learning, serta evaluasi performa dengan confussion matrix. Hasil menunjukkan bahwa EfficientNetV2B0 mencapai akurasi 99.67%, lebih tinggi dibandingkan Xception dengan 98.33%, serta memiliki waktu training yang lebih efisien. Kedua model kemudian diintegrasikan ke dalam aplikasi berbasis web untuk mempermudah klasifikasi tumbuhan obat secara praktis dan akurat.References
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