Pemodelan Klasifikasi Penyakit Daun Tanaman Tomat dengan Convolutional Neural Network Algorithm
DOI:
https://doi.org/10.30865/klik.v4i5.1646Keywords:
Convolutional Neural Network; Disease Classification; Tomato Leaves; Dataset PlantVillage; Validation AccuracyAbstract
Tomatoes constitute a horticultural crop with significant economic value and are extensively cultivated by the people of Indonesia, an agricultural country. Tomato productivity is anticipated to increase in 2021-2022. However, issues often arise during the plant care process when attempting to identify the type of disease affecting the leaves of tomato plants, owing to the nearly identical symptoms that make differentiation challenging. Tomato plant leaves play a crucial role in the plant growth process. Addressing the challenge of tomato leaf diseases necessitates a method that employs specific technology for easy detection. Before implementing this method, a classification model for tomato leaf plants must be developed. The goal of this modeling is to create an effective tomato leaf classification model for subsequent use in a disease detection system that utilizes IoT technology. The modeling of tomato plant disease classification involves the application of the Convolutional Neural Network algorithm. The modeling process encompasses data source analysis, data preprocessing, model formation, model training, model evaluation, and the subsequent acquisition of model results. The dataset utilized for implementation comprises 18,162 images of tomatoes. The accuracy of the training data surpasses that of the validation data, indicating the reliability of the data used for model development. The accuracy of the training data is recorded at 94.06%, signifying its suitability for modeling. Additionally, the loss function result on the training data is 7.8%, further affirming the quality of the training data for model development.
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R. C. Sigitta, R. H. Saputra, and F. Fathulloh, “Deteksi Penyakit Tomat melalui Citra Daun menggunakan Metode Convolutional Neural Network,” Avitec, vol. 5, no. 1, p. 43, 2023, doi: 10.28989/avitec.v5i1.1404.
P. Palupiningsih, A. R. Sujiwanto, and R. R. B. P. Prawirodirjo, “Analisis Perbandingan Performa Model Klasifikasi Kesehatan Daun Tomat menggunakan arsitektur VGG, MobileNet, dan Inception V3,” J. Ilmu Komput. dan Agri-Informatika, vol. 10, no. 1, pp. 98–110, 2023, doi: 10.29244/jika.10.1.98-110.
F. Felix, S. Faisal, T. F. M. Butarbutar, and P. Sirait, “Implementasi CNN dan SVM untuk Identifikasi Penyakit Tomat via Daun,” J. SIFO Mikroskil, vol. 20, no. 2, pp. 117–134, 2019, doi: 10.55601/jsm.v20i2.670.
C. Senthilkumar and M. Kamarasan, “a Survey on Leaf Disease Detection Using Image Processing Techniques,” vol. 5, no. 7, pp. 889–900, 2018, [Online]. Available: www.jetir.org
S. Pouyanfar et al., “Dynamic Sampling in Convolutional Neural Networks for Imbalanced Data Classification,” Proc. - IEEE 1st Conf. Multimed. Inf. Process. Retrieval, MIPR 2018, pp. 112–117, 2018, doi: 10.1109/MIPR.2018.00027.
A. Saxena, “An Introduction to Convolutional Neural Networks,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 10, no. 12, pp. 943–947, 2022, doi: 10.22214/ijraset.2022.47789.
A. Salam, F. Yanto, S. Agustian, and S. Ramadhani, “Perbandingan Klasifikasi Citra CT-Scan Kanker Paru-Paru Menggunakan Contrast Stretching Pada CNN dengan EfficientNet-B0,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 4, no. 3, pp. 1341–1351, 2023, doi: 10.30865/klik.v4i3.1514.
A. A. Hipzi, G. W. Wiriasto, I. Made, and B. Suksmadana, “Klasifikasi Pneumonia Pada Augmentasi Citra X-Ray Paru-Paru Menggunakan Metode Convolution Neural Network (Cnn),” 2023, [Online]. Available: http://eprints.unram.ac.id/id/eprint/39247
I. M. D. Maysanjaya, “Klasifikasi Pneumonia pada Citra X-rays Paru-paru dengan Convolutional Neural Network,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 9, no. 2, pp. 190–195, 2020, doi: 10.22146/jnteti.v9i2.66.
J. Yopento, E. Ernawati, and F. F. Coastera, “Identifikasi Pneumonia Pada Citra X-Ray Paru-Paru Menggunakan Metode Convolutional Neural Network (CNN) Berdasarkan Ekstraksi Fitur Sobel,” Rekursif J. Inform., vol. 10, no. 1, pp. 40–47, 2022, doi: 10.33369/rekursif.v10i1.17247.
S. Lasniari, J. Jasril, S. Sanjaya, F. Yanto, and M. Affandes, “Klasifikasi Citra Daging Babi dan Daging Sapi Menggunakan Deep Learning Arsitektur ResNet-50 dengan Augmentasi Citra,” J. Sist. Komput. dan Inform., vol. 3, no. 4, p. 450, 2022, doi: 10.30865/json.v3i4.4167.
H. Almirza, S. Sanjaya, L. Handayani, and F. Syafria, “Klasifikasi Daging Sapi dan Daging Babi Menggunakan Convolutional Neural Network EfficientNet-B0 dengan Augmentasi Citra,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 3, no. 6, pp. 1013–1021, 2023, doi: 10.30865/klik.v3i6.910.
I. A. DLY, Jasril, S. Sanjaya, L. Handayani, and F. Yanto, “Klasifikasi Citra Daging Sapi dan Babi Menggunakan CNN Alexnet dan Augmentasi Data,” J. Inf. Syst. Res., vol. 4, no. 4, pp. 1176–1185, 2023, doi: 10.47065/josh.v4i4.3702.
D. Putri Ayuni, Jasril, M. Irsyad, F. Yanto, and S. Sanjaya, “Augmentasi Data Pada Implementasi Convolutional Neural Network Arsitektur Efficientnet-B3 Untuk Klasifikasi Penyakit Daun Padi,” Zo. J. Sist. Inf., vol. 5, no. 2, pp. 239–249, 2023, doi: 10.31849/zn.v5i2.13874.
N. H. Putri, Jasril, M. Irsyad, S. Agustian, and F. Yanto, “Klasifikasi Citra Stroke Menggunakan Augmentasi dan Convolutional Neural Network EfficientNet-B0,” J. Media Inform. Budidarma, vol. 7, no. 2, pp. 650–658, 2023, doi: 10.30865/mib.v7i2.5981.
A. Akram, K. Fayakun, and H. Ramza, “Klasifikasi Hama Serangga pada Pertanian Menggunakan Metode Convolutional Neural Network,” Build. Informatics, Technol. Sci., vol. 5, no. 2, pp. 397–406, 2023, doi: 10.47065/bits.v5i2.4063.
M. Agarwal, A. Singh, S. Arjaria, A. Sinha, and S. Gupta, “ToLeD: Tomato Leaf Disease Detection using Convolution Neural Network,” Procedia Comput. Sci., vol. 167, no. 2019, pp. 293–301, 2020, doi: 10.1016/j.procs.2020.03.225.
S. Ashok, G. Kishore, V. Rajesh, S. Suchitra, S. G. Gino Sophia, and B. Pavithra, “Tomato leaf disease detection using deep learning techniques,” Proc. 5th Int. Conf. Commun. Electron. Syst. ICCES 2020, no. Icces, pp. 979–983, 2020, doi: 10.1109/ICCES48766.2020.09137986.
R. Soekarta, N. Nurdjan, and A. Syah, “Klasifikasi Penyakit Tanaman Tomat Menggunakan Metode Convolutional Neural Network (CNN),” Insect (Informatics Secur. J. Tek. Inform., vol. 8, no. 2, pp. 143–151, 2023, doi: 10.33506/insect.v8i2.2356.
N. Awalia, “Identifikasi Penyakit Leaf Mold Pada Daun Tomat Menggunakan Model Densenet121 Berbasis Transfer Learning,” J. Ilm. Ilmu Komput., vol. 8, no. 1, pp. 49–52, 2022, doi: 10.35329/jiik.v8i1.212.
A. Lawi, N. S. Intizhami, R. Mukhtarom, and S. Amir, “Klasifikasi Penyakit Citra Daun Tanaman Tomat Dengan Ensemble Convolutional Neural Network,” Sntei, pp. 207–212, 2022.
K. KC, Z. Yin, M. Wu, and Z. Wu, “Depthwise separable convolution architectures for plant disease classification,” Comput. Electron. Agric., vol. 165, no. December 2018, p. 104948, 2019, doi: 10.1016/j.compag.2019.104948.
D. P. Hughes and M. Salathe, “An open access repository of images on plant health to enable the development of mobile disease diagnostics,” 2015, [Online]. Available: http://arxiv.org/abs/1511.08060
O. Attallah, “Tomato Leaf Disease Classification via Compact Convolutional Neural Networks with Transfer Learning and Feature Selection,” Horticulturae, vol. 9, no. 2, 2023, doi: 10.3390/horticulturae9020149.
S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” Proc. 2017 Int. Conf. Eng. Technol. ICET 2017, vol. 2018-Janua, pp. 1–6, 2017, doi: 10.1109/ICEngTechnol.2017.8308186.
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