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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) The IJICS (International Journal of Informatics and Computer Science) JURNAL MAHAJANA INFORMASI Jatilima : Jurnal Multimedia Dan Teknologi Informasi Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Scientia Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Indonesian Journal of Education And Computer Science Jurnal Ilmiah Multidisiplin Indonesia International Journal of Industrial Innovation and Mechanical Engineering International Journal of Information Engineering and Science Bulletin of Engineering Science, Technology and Industry
Darmeli Nasution
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Physics Social Sciences

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Journal : Jurnal Sistem Komputer dan Informatika (JSON)

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Klasifikasi Penyakit Tanaman Daun Tomat Menggunakan Transfer Learning Andriani, Tuti; Iqbal, Muhammad; Darmeli Nasution
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 6 No. 4 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v6i4.8948

Abstract

Leaf diseases in tomato plants can significantly reduce crop yields and threaten agricultural sustainability. This study proposes a multi-class classification approach for tomato leaf diseases using transfer learning with pre-trained CNN architectures, specifically DenseNet121 and DenseNet169. The dataset used is a subset of PlantVillage, consisting of six disease classes and healthy leaves, with preprocessing steps including image augmentation and resizing. The training strategy involves two phases: feature extraction and fine-tuning, optimized using the Adam algorithm and categorical cross-entropy loss function. Evaluation metrics such as accuracy, precision, recall, and F1-score show that the DenseNet121 model achieves the best performance, reaching an accuracy of 96.23%, followed by MobileNetV2 with 92.89%. Loss curves and confusion matrix analysis confirm that the model performs classification tasks with stability and high precision, despite some misclassifications between visually similar disease classes. This study demonstrates that transfer learning with DenseNet—particularly DenseNet121 is effective for automatic and efficient classification of various tomato leaf diseases, offering potential for real-world implementation as a computer vision-based plant disease diagnosis system.
Co-Authors Amrizal Lubis Asri Santosa Bela Firmantoyo Eko Hariyanto Eswin Syahputra IQBAL , MUHAMMAD Irwan Jesica Uli Panggabean Juliyandri Saragih Khairul Khairul Khairul, Khairul Khumairoh, Annisa Ludfia Anggi Safitri Sinaga Muhammad Iqbal Muhammad Iqbal Muhammad Syahputra Novelan Muhammad Wahyudi Mutiara Widasari Sitopu Rian Farta Wijaya Sinambela, Sugi Hartono Sipra Barutu Sirait, Donna Nurhaida Masdiana Siregar, Andree Rizky Yuliansyah Sitorus , Zulham Sitorus, Zulham Sugeng Pranoto Tiara Sylvia Titin Mega Andini Siahaan Tuti Andriani Yoga Yuniadi Zhafirah Rizki Fadilah Lbs Zuhri Ramadhan