PERBANDINGAN SUPPORT VECTOR MACHINE DAN CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI PENYAKIT DAUN SINGKONG
DOI:
https://doi.org/10.31539/v7atvm18Abstract
Tanaman singkong merupakan salah satu komoditas pertanian penting di Indonesia, namun produksinya sering menurun akibat serangan penyakit seperti CBB, CBSD, CGM, dan CMD. Penelitian ini bertujuan untuk membandingkan dua metode, yaitu Support Vector Machine (SVM) dan Convolutional Neural Network (CNN), dalam klasifikasi penyakit daun singkong berbasis citra digital. Citra daun diubah menjadi ukuran 128×128 piksel untuk menjaga keseimbangan antara detail visual dan efisiensi komputasi. SVM menggunakan ekstraksi fitur HOG, sedangkan CNN melakukan ekstraksi fitur secara otomatis melalui lapisan konvolusi. Hasil pengujian menunjukkan bahwa SVM memperoleh akurasi sebesar 88%, sedangkan CNN mencapai 84%. SVM menunjukkan kinerja yang lebih tinggi dan stabil pada seluruh metrik, sedangkan CNN mampu mempelajari pola visual secara otomatis meskipun hasilnya sedikit lebih rendah pada dataset ini. Dengan demikian, metode SVM dinilai lebih efektif untuk klasifikasi penyakit daun singkong.
Kata Kunci: Penyakit Daun Singkong, CNN, EfficientNet-B0, Klasifikasi Citra, SVM
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