Implementasi Pengenalan Daun Toga Menggunakan MobilenetV2
DOI:
https://doi.org/10.29407/jr07we81Abstract
Tanaman Obat Keluarga (TOGA) merupakan tanaman yang banyak dimanfaatkan sebagai bahan pengobatan tradisional dan pemeliharaan kesehatan. Namun, proses identifikasi tanaman TOGA berdasarkan karakteristik daun masih sering mengalami kesalahan karena adanya kemiripan bentuk antar jenis tanaman. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi daun tanaman TOGA berbasis citra digital menggunakan arsitektur MobileNetV2. Dataset yang digunakan terdiri dari empat jenis tanaman TOGA, yaitu jahe, kunyit, sereh, dan temulawak dengan total 1.248 citra daun. Tahapan penelitian meliputi pengumpulan dataset, prapemrosesan citra berupa resizing, normalisasi, dan augmentasi data, serta pelatihan model menggunakan pendekatan transfer learning pada MobileNetV2. Evaluasi model dilakukan menggunakan confusion matrix dengan metrik accuracy, precision, recall, dan F1-score. Hasil pengujian menunjukkan bahwa model memperoleh nilai validation accuracy sebesar 97,98% dengan validation loss sebesar 0,0466. Nilai rata-rata precision, recall, dan F1-score yang diperoleh masing-masing sebesar 98%. Hasil tersebut menunjukkan bahwa MobileNetV2 mampu mengklasifikasikan daun tanaman TOGA dengan tingkat akurasi yang tinggi dan berpotensi diterapkan pada sistem identifikasi tanaman berbasis web
Keywords:
Deep Learning, Klasifikasi Citra, MobileNetV2, TOGA, Web##plugins.themes.default.displayStats.downloads##
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