EVALUASI KLASSIFIKASI PENYAKIT DAUN TEH MENGGUNAKAN TRANSFER LEARNING EFFICIENTNETB0

santowi azis; bambang irawan

doi:10.23960/jitet.v14i1.8954

Authors

santowi azis mahasiswa
bambang irawan

DOI:

https://doi.org/10.23960/jitet.v14i1.8954

Abstract Views: 62 File Views: 41

Keywords:

Penyakit Daun Teh, Convolutional Neural Network, EfficientNetB0, Transfer Learning, Klasifikasi Citra

Abstract

Penelitian ini mengevaluasi performa model EfficientNetB0 berbasis transfer learning untuk deteksi dini penyakit daun teh. Dataset Tea Leaf Disease yang tersedia secara publik digunakan, terdiri dari 5.867 gambar daun teh dengan enam kelas, yaitu algal spot, brown blight, gray blight, healthy, helopeltis, dan red spot. Dataset dibagi menjadi data latih (70%), validasi (15%), dan uji (15%). Model dilatih selama 30 epoch dengan laju pembelajaran 1×10⁻⁴, kemudian dilakukan fine-tuning selama 15 epoch tambahan menggunakan laju pembelajaran 1×10⁻⁵ disertai augmentasi data yang intensif. Hasil pengujian pada data uji menunjukkan akurasi sebesar 97%, dengan nilai macro-averaged precision, recall, dan F1-score masing-masing mencapai 0,97. Analisis confusion matrix mengindikasikan tingkat kesalahan klasifikasi yang rendah, meskipun masih terjadi kesalahan pada kelas-kelas yang memiliki kemiripan visual tinggi, seperti brown blight dengan gray blight serta helopeltis dengan healthy. Hasil ini menunjukkan bahwa EfficientNetB0 memiliki akurasi dan efisiensi yang tinggi, sehingga berpotensi untuk diimplementasikan pada aplikasi mobile sebagai sistem pendukung deteksi dini penyakit daun teh bagi petani.

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