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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Turahman, Tazkira
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Analisis Perbandingan Metode Convolutional Neural Network (CNN) dan MobileNet dalam Klasifikasi Penyakit Daun Padi Turahman, Tazkira; Hasmin, Erfan; Aryasa, Komang
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 1 (2025): JANUARI-MARET 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i1.3218

Abstract

This study aims to compare the effectiveness of Convolutional Neural Networks (CNN) and MobileNet in classifying rice leaf diseases (Oryza sativa), such as bacterial blight, brown spot, and leaf smut. The use of a dataset from Kaggle facilitates the performance evaluation of both models. The results show that MobileNet achieved a higher accuracy of 94.79% in just 10 epochs, while CNN reached 90.24% after 150 epochs. MobileNet’s efficiency in terms of training time and performance is superior to CNN. This study recommends using MobileNet for similar applications and further research with an expanded dataset and other deep learning methods.
Co-Authors Erfan Hasmin Komang Aryasa