Fusion Fitur EfficientNetB0 dan MobileNetV2 dengan Machine Learning untuk Klasifikasi Penyakit Daun Kubis
DOI:
https://doi.org/10.54065/artificial.2021Keywords:
Daun Kubis, Efficientnetb0, Feature Fusion, Mobilenetv2, Support Vector MachineAbstract
Penyakit pada daun kubis memiliki karakteristik visual yang dapat saling menyerupai sehingga proses identifikasi berbasis citra menjadi tantangan dalam membedakan daun sehat dan daun yang terinfeksi penyakit. Penelitian ini bertujuan mengembangkan metode klasifikasi penyakit daun kubis menggunakan feature fusion EfficientNetB0 dan MobileNetV2 yang dikombinasikan dengan algoritma machine learning. Dataset yang digunakan terdiri atas 1.022 citra dengan tiga kelas, yaitu Daun Sehat, Erwinia carotovora, dan Xanthomonas campestris. Citra melalui tahap preprocessing berupa resize menjadi 224×224 piksel dan normalisasi nilai piksel, kemudian dibagi secara stratified dengan rasio 80:10:10. EfficientNetB0 dan MobileNetV2 digunakan sebagai feature extractor dengan bobot pralatih, kemudian masing-masing menghasilkan 1.280 fitur yang digabungkan menjadi 2.560 fitur. Fitur gabungan diklasifikasikan menggunakan Support Vector Machine (SVM) dan Random Forest. Hasil pengujian pada 103 citra testing menunjukkan bahwa SVM memperoleh akurasi 96,12% dan macro F1-score 0,94, sedangkan Random Forest memperoleh akurasi 94,17% dan macro F1-score 0,93. SVM juga mampu mengklasifikasikan seluruh citra Daun Sehat dengan benar. Hasil tersebut menunjukkan bahwa feature fusion EfficientNetB0 dan MobileNetV2 yang dikombinasikan dengan SVM dapat digunakan untuk klasifikasi penyakit daun kubis, meskipun pembedaan kelas Erwinia carotovora masih memerlukan pengembangan lebih lanjut.
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