Penerapan Metode Convolutional Neural Network untuk Klasifikasi Penyakit Daun Apel pada Imbalanced Data

Authors

  • Chilyatun Nisa' UPN "Veteran" Jawa Timur
  • Eva Yulia Puspaningrum UPN "Veteran" Jawa Timur
  • Hendra Maulana

DOI:

https://doi.org/10.33005/santika.v1i0.46

Keywords:

Penyakit Daun Apel, Klasifikasi Citra Digital, Convolutional Neural Network, InceptionV3, Confusion Matrix

Abstract

Menurut data produksi buah-buahan di Indonesia yang dipublikasikan oleh BPS, produksi apel pada tahun 2017 mengalami penurunan sebesar 3.3% atau sejumlah 10.780 ton dari tahun 2016 yang menghasilkan sebanyak 329.780 ton. Hal itu disebabkan oleh berbagai penyakit yang sering terjadi pada produksi apel, oleh karena itu pendeteksian penyakit daun apel yang tepat waktu menjadi sangat penting untuk industri apel yang berkembang dengan sehat. Sehingga dibutuhkan sistem yang efektif seperti klasifikasi citra digital pada tanaman. Metode yang digunakan pada penelitian ini merupakan adalah Convolutional Neural Network (CNN) dengan arsitektur InceptionV3. Penelitian ini menggunakan dataset Plant Pathology 2020 - FGV C7 sebanyak 1821 data citra dengan 4 kelas. Data dibagi menjadi 3 set data (latih, validasi, dan uji) dengan rasio 70:10:20. Hasil pengujian dievaluasi dengan menggunakan data uji, untuk proses evaluasi menggunakan confusion matrix. Berdasarkan hasil pelatihan mencapai akurasi 96.37%. Pad pengujian hasil akurasi pada masing-masing kelas sebesar 90,6%, 62,3%, 94,3%, dan 92%.

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Published

2020-11-01

How to Cite

Nisa’, C., Puspaningrum, E. Y., & Maulana, H. (2020). Penerapan Metode Convolutional Neural Network untuk Klasifikasi Penyakit Daun Apel pada Imbalanced Data. Prosiding Seminar Nasional Informatika Bela Negara, 1, 169–175. https://doi.org/10.33005/santika.v1i0.46

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