IDENTIFIKASI PENYAKIT LEAF MOLD PADA DAUN TOMAT MENGGUNAKAN MODEL DENSENET121 BERBASIS TRANSFER LEARNING
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Published:
Apr 18, 2022
Keywords:
Transfer Learning, DenseNet, Convolutional Neural Network, Penyakit Leaf Mold.
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Abstract
Penyakit leaf mold pada daun tomat merupakan penyakit bercak daun yang disebabkan oleh jamur Cladosporium fulvum. penyakit tersebut biasanya terjadi pada tomat yang dibudidayakan dalam lingkungan lembab. Gejala penyakit yang sulit terdeteksi secara manual, dapat menyebabkan penurunan kualitas dan hasil panen tomat selama 10 tahun terakhir. Penelitian ini bertujuan untuk mengidentifikasi penyakit leaf mold pada daun tomat menggunakan pre-trained model convolutional neural network. Model tersebut yaitu DenseNet121 dengan teknik transfer learning. Penelitian ini menggunakan sebanyak 2.283 dataset citra daun tomat, yang terdiri dari 3 jenis kelas prediksi diantaranya yaitu penyakit leaf mold, daun tomat sehat, dan penyakit tomat lainnya yang dimasukan kedalam kelas prediksi penyakit lainnya. Hasil penelitian diperoleh model A sebagai model terbaik diantara 3 model yang diuji coba, dengan nilai akurasi, precision, dan recall yang dihasilkan yaitu sebesar 92,6%, 93,3%, dan 93%.
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References
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[3] A. Peryanto, A. Yudhana, and R. Umar, “Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation,” vol. 4, no. 1, pp. 45–51, 2020, doi: https://doi.org/10.30871/jaic.v4i1.2017.
[4] N. K. Chauhan and K. Singh, “A review on conventional machine learning vs deep learning,” in 2018 International Conference on Computing, Power and Communication Technologies, GUCON 2018, 2019, pp. 347–352, doi: https://doi.org/10.1109/GUCON.2018.8675097.
[5] F. Alviansyah, I. Ruslianto, and M. Diponegoro, “Identifikasi Penyakit Pada Tanaman Tomat Berdasarkan Warna Dan Bentuk Daun Dengan Metode Naive Bayes Classifier Berbasis Web,” vol. 05, no. 1, 2017, doi: http://dx.doi.org/10.26418/coding.v5i1.19171.
[6] M. F. Susila, B. Irawan, and C. Setianingsih, “Deteksi Penyakit Pada Daun Pakcoy Dengan Pengolahan Citra Menggunakan Metode Convolutional Neural Network,” in e-Proceeding of Engineering, 2020, vol. 7, no. 3, pp. 9347–9354.
[7] L. D. Nguyen, D. Lin, Z. Lin, and J. Cao, “Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation,” Proc. - IEEE Int. Symp. Circuits Syst., vol. 2018-May, pp. 3–7, 2018, doi: https://doi.org/10.1109/ISCAS.2018.8351550.
[8] A. Hidayatuloh, M. Nursalman, and E. Nugraha, “Identification of Tomato Plant Diseases by Leaf Image Using Squeezenet Model,” in 2018 International Conference on Information Technology Systems and Innovation, ICITSI 2018 - Proceedings, 2018, pp. 199–204, doi: https://doi.org/10.1109/ICITSI.2018.8696087.
[9] S. Ilahiyah and A. Nilogiri, “Implementasi Deep Learning Pada Identifikasi Jenis Tumbuhan Berdasarkan Citra Daun Menggunakan Convolutional Neural Network,” JUSTINDO (Jurnal Sist. dan Teknol. Inf. Indones., vol. 3, no. 2, pp. 49–56, 2018.
[10] A. Santoso and G. Ariyanto, “Implementasi Deep Learning Berbasis Keras Untuk Pengenalan Wajah,” Emit. J. Tek. Elektro, vol. 18, no. 01, pp. 15–21, 2018, doi: https://doi.org/10.23917/emitor.v18i01.6235.
[11] U. Michelucci, Applied Deep Learning : A Case-Based Approach to Understanding Deep Neural Networks. New York: Springer Science and Business Media, 2018.
[12] G. Geetharamani and A. P. J., “Identification of plant leaf diseases using a nine-layer deep convolutional neural network,” Comput. Electr. Eng., vol. 76, pp. 323–338, 2019, doi: https://doi.org/10.1016/j.compeleceng.2019.04.011.
[13] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 2261–2269, 2017, doi: https://doi.org/10.1109/CVPR.2017.243.
[14] A. L. Unihehu and I. Suharjo, “Klasifikasi Jenis Ikan Berbasis Jaringan Saraf Tiruan Menggunakan Algoritma Principal Component Analysis (PCA),” J. Ilm. Ilmu Komput. Fak. Ilmu Komput. Univ. Al Asyariah Mandar, vol. 7, no. 2, pp. 27–32, 2021, doi: https://doi.org/10.35329/jiik.v7i2.200.