Pemanfaatan Metode CNN Menggunakan Arsitektur Alexnet untuk  Peningkatan Kinerja Klasifikasi Penyakit Daun Tomat

Dwi Puji Prabowo; Henry Bastian; Ali Muqoddas; Ricardus Anggi Pramunendar; Feri Agustina

doi:10.24176/simet.v15i2.12529

Pemanfaatan Metode CNN Menggunakan Arsitektur Alexnet untuk Peningkatan Kinerja Klasifikasi Penyakit Daun Tomat

Dwi Puji Prabowo, Henry Bastian, Ali Muqoddas, Ricardus Anggi Pramunendar, Feri Agustina

Sari

Tomat adalah salah satu komoditas hortikultura dengan nilai ekonomi yang tinggi, tantang yang dihadapi oleh petani salah satunya dalah kerentanan penyakit tomat terhadap penyakit. Identifikasi secara visual pada daun sulit diuraikan dengan sekali pandang, sehingga menyebabkan asumsi yang tidak akurat tentang penyakit tersebut. Akibatnya, mekanisme pencegahan yang dilakukan petani menjadi tidak efektif dan berdampak merugikan. Penelitian ini mengusulkan identifikasi penyakit tomat secara automatis menggunakan metode Convolution Neural Network. Dalam makalah ini kami melakukan evaluasi pada metode CNN dengan arsitektur Alexnet dengan konfigurasi layer untuk mencari hasil kinerja terbaik dari penggunaan parameter tersebut pada architektur Alexnet. Pada penelitian ini juga melakukan analisis yang diperoleh dari hubungan antara parameter yang digunakan terhadap kinerja akurasi, dan analisis terhadap dampak penggunaan parameter dengan jumlah dataset daun tomat dari dataset PlantVillage.

Kata Kunci

tomat;CNN;alexnet;layer;parameter

Teks Lengkap:

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Referensi

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DOI: https://doi.org/10.24176/simet.v15i2.12529