Klasifikasi Penyakit Daun Tanaman Berbasis Citra Menggunakan Convolutional Neural Network Data Augmentation

Bintang Dyas Suci, Musfiroh Musfiroh, Shintia Putriayu Sefriani, Sumanto Sumanto, Roida Pakpahan, Imam Budiawan

Abstract


Penelitian ini membahas penerapan Convolutional Neural Network (CNN) yang dikombinasikan dengan teknik augmentasi data untuk klasifikasi penyakit daun tanaman berbasis citra. Permasalahan utama penelitian ini adalah keterbatasan jumlah data latih yang dapat memengaruhi kinerja model klasifikasi. Tujuan penelitian adalah mengevaluasi efektivitas augmentasi data dalam meningkatkan performa model CNN pada dataset berskala terbatas. Dataset yang digunakan adalah Plant Disease Recognition Dataset yang terdiri dari 1.523 citra dengan tiga kelas, yaitu Healthy, Powdery Mildew, dan Rust. Penelitian ini menggunakan metode eksperimen dengan tahapan praproses data, augmentasi data, pelatihan model, serta evaluasi performa yang seluruhnya dilakukan menggunakan Google Colab. Teknik augmentasi yang diterapkan meliputi rotasi, zoom, dan horizontal flip. Hasil penelitian menunjukkan bahwa model CNN mampu mencapai akurasi validasi yang baik, meskipun performa klasifikasi antar kelas masih bervariasi, khususnya pada kelas Rust yang memiliki karakteristik visual kompleks, sebagaimana ditunjukkan melalui confusion matrix dan classification report. Selain itu, penelitian ini mengimplementasikan skema prediksi real-time sebagai proof-of-concept. Secara keseluruhan, hasil penelitian menunjukkan bahwa kombinasi CNN dan augmentasi data efektif untuk klasifikasi penyakit tanaman pada kondisi keterbatasan data dan sumber daya komputasi.

Keywords


Convolutional Neural Network, klasifikasi citra, penyakit daun tanaman, augmentasi data, deep learning

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DOI: https://doi.org/10.30998/semnasristek.v10i1.8894

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