Analisis Kinerja Convolutional Neural Networks Baseline untuk Identifikasi Jenis Jenis Penyakit Kentang: Performance Analysis of Baseline Convolutional Neural Networks for Identifying Potato Disease Types

Khoir Prasetyo; Ridwan Mahenra

doi:10.57152/malcom.v5i2.1722

Authors

Khoir Prasetyo Universitas Teknokrat Indonesia
Ridwan Mahenra Universitas Teknokrat Indonesia

DOI:

https://doi.org/10.57152/malcom.v5i2.1722

Keywords:

Citra Daun Kentang, Convolutional Neural Networks, Deep Learning, Identifikasi Penyakit Tanaman

Abstract

Penelitian ini bertujuan untuk mengembangkan dan mengevaluasi model Convolutional Neural Network (CNN) baseline dalam mengidentifikasi jenis-jenis penyakit pada daun kentang. Dataset yang digunakan terdiri dari citra daun kentang yang terinfeksi dan sehat, yang diklasifikasikan ke dalam beberapa kategori penyakit seperti late blight, early blight, dan penyakit bakteri. Model CNN dirancang dengan arsitektur dasar yang meliputi beberapa lapisan konvolusi, pooling, dan fully connected, serta dilatih menggunakan Optimizer Adam dengan fungsi loss categorical cross-entropy. Hasil evaluasi menunjukkan bahwa model mencapai akurasi 82% pada validation set dan rata-rata 95% pada data acak. Meskipun model menunjukkan performa yang baik dalam mengklasifikasikan citra, indikasi overfitting terlihat dari perbedaan antara akurasi training dan validation. Analisis lebih lanjut mengidentifikasi kesalahan prediksi yang terjadi, terutama pada kelas dengan gejala visual yang mirip. Penelitian ini merekomendasikan penerapan teknik regulasi, augmentasi data, dan penggunaan arsitektur lebih kompleks untuk meningkatkan akurasi model. Hasil penelitian ini diharapkan dapat memberikan kontribusi bagi pengembangan sistem deteksi penyakit tanaman berbasis kecerdasan buatan yang lebih efisien.

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