Performance Analysis of Convolutional Neural Networks and Naive Bayes Methods for Disease Classification in Tomato Plant Leaves
DOI:
https://doi.org/10.56705/ijodas.v6i3.255Keywords:
Convolutional Neural Network (CNN), Naive Bayes, Tomato Leaf DiseaseAbstract
Tomatoes are one of the most widely cultivated and consumed crops, but they are highly susceptible to disease attacks. The main diseases that often attack tomato plants are early blight and late blight. This study compares two machine learning-based classification methods, namely Convolutional Neural Network (CNN) and Naïve Bayes, in detecting tomato leaf diseases. The dataset used consists of 1,255 images obtained from Kaggle, which have been processed and divided into three data ratio scenarios (70:30, 80:20, and 90:10) for training and testing. The results showed that CNN is superior to Naïve Bayes, with the highest accuracy reaching 83.01%, while Naïve Bayes only achieved 34%. With better stability and accuracy, CNN has the potential to help farmers detect diseases more quickly and increase agricultural productivity
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[1] A. Universitas and S. Ratulangi, “Jurnal Agroekoteknologi,” vol. 4, pp. 84–93, 2023.
[2] Nining Putri Ningsih, Emi Suryadi, Lalu Darmawan Bakti, and Bahtiar Imran, “Klasifikasi Penyakit Early Blight Dan Late Blight Pada Tanaman Tomat Berdasarkan Citra Daun Menggunakan Metode Cnn Berbasis Website,” J. Kecerdasan Buatan dan Teknol. Inf., vol. 1, no. 3, pp. 27–35, 2022, doi: 10.69916/jkbti.v1i3.10.
[3] Lely Sahrani, “Klasifikasi Penyakit Daun Tomat Berdasarkan Ekstraksi Tekstur Daun Menggunakan Gabor Filter Dan Algoritma Support Vector Machine,” Repos. Univ. Islam Negri Sumatera Utara Medan, pp. 1–100, 2021.
[4] L. G. Mugao, B. M. Gichimu, P. W. Muturi, and E. K. Njoroge, “Essential Oils as Biocontrol Agents of Early and Late Blight Diseases of Tomato under Greenhouse Conditions,” vol. 2021, 2021, doi: 10.1155/2021/5719091.
[5] N. Awalia and A. Primajaya, “Identifikasi Penyakit Leaf Mold Daun Tomat Menggunakan Model DenseNet- 121,” J. Ilm. Ilmu Komput., vol. 8, no. 1, pp. 49–54, 2022, [Online]. Available: http://ejournal.fikom- unasman.ac.id
[6] 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.
[7] F. A. , I. R. , Muhammad Diponegoro, “Identifikasi Penyakit Pada Tanaman Tomat Berdasarkan Warna Dan Bentuk Daun Dengan Metode Naive Bayes Classifier Berbasis Web,” Coding J. Komput. dan Apl., vol. 5, no. 1, 2017, doi: 10.26418/coding.v5i1.19171.
[8] Dian, Purnawansyah, H. Darwis, and L. Nurhayati, “Klasifikasi Penyakit Bawang Merah Menggunakan Naïve Bayes dan Convolutional Neural Network,” Indones. J. Comput. Sci., vol. 12, no. 4, pp. 1932–1943, 2023, doi: 10.33022/ijcs.v12i4.3265.
[9] H. Apriyani and K. Kurniati, “Perbandingan Metode Naïve Bayes Dan Support Vector Machine Dalam Klasifikasi Penyakit Diabetes Melitus,” J. Inf. Technol. Ampera, vol. 1, no. 3, pp. 133–143, 2020, doi: 10.51519/journalita.volume1.isssue3.year2020.page133-143.
[10] M. Khan, F. Gulan, and A. Riaz, “Early and late blight disease identi fi cation in tomato plants using a neural network-based model to augmenting agricultural productivity,” vol. 107, no. 3, pp. 1–27, 2024, doi: 10.1177/00368504241275371.
[11] B. P. Esther, J. S. Paul, S. Sunil, and S. Pandey, “Detection of Diseases in Plant Leaf Using CNN Technique,”
vol. 14, no. 03, pp. 878–884, 2022.
[12] V. S. Dhaka, S. V. Meena, G. Rani, D. Sinwar, M. F. Ijaz, and M. Wo, “A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases,” 2021.
[13] C. U. Aji, Wasito Galih, “Jurnal Teknologi Terpadu Learning,” J. Teknol. Terpadu, vol. 8, no. 1, pp. 89– 94, 2022.
[14] Sandy Andika Maulana, Shabrina Husna Batubara, Tasya Ade Amelia, and Yohanna Permata Putri Pasaribu, “Penerapan Metode CNN (Convolutional Neural Network) Dalam Mengklasifikasi Jenis Ubur-Ubur,” J. Penelit. Rumpun Ilmu Tek., vol. 2, no. 4, pp. 122–130, 2023, doi: 10.55606/juprit.v2i4.3084.
[15] J. Arfah, Purnawansyah, H. Darwis, and R. Sastra, “Klasifikasi Penyakit Bawang Merah Menggunakan Naive Bayes dan CNN dengan Fitur GLCM,” Indones. J. Comput. Sci., vol. 12, no. 3, pp. 1231–1240, 2023, doi: 10.33022/ijcs.v12i3.3236.
[16] A. Nurjulianty, P. Purnawansyah, and H. Darwis, “Perbandingan Metode Naïve Bayes dan K-NN dengan Ekstraksi Fitur GLCM pada Klasifikasi Daun Herbal,” J. Media Inform. Budidarma, vol. 7, no. 4, p. 1740, 2023, doi: 10.30865/mib.v7i4.6262.
[17] A. Agustina, F. Yanto, E. Budianita, I. Iskandar, and F. Syafria, “Klasifikasi Penyakit Tanaman Padi Menggunakan Metode Cnn Arsitektur Densenet-121 Dan Augmentasi Data,” J. Inf. Syst. Informatics Eng., vol. 8, no. 1, pp. 124–134, 2024.
[18] A. Nurdin, D. Satria, Y. Kartika, A. Rezha, and E. Najaf, “Klasifikasi Penyakit Daun Tomat Dengan Metode
Convolutional Neural Network Menggunakan Arsitektur Inception-V3,” no. 1, pp. 1–6, 2024.
[19] I. As’ad, “Advancing Healthcare Diagnostics: A Study on Gaussian Naive Bayes Classification of Blood Samples,” Int. J. Artif. Intell. Med. Issues, vol. 1, no. 2, pp. 115–123, 2023, doi: 10.56705/ijaimi.v1i2.120.
[20] A. B. Prakosa, Hendry, and R. Tanone, “Implementasi Model Deep Learning Convolutional Neural Network (CNN) Pada Citra Penyakit Daun Jagung Untuk Klasifikasi Penyakit Tanaman,” J. Pendidik. Teknol. Inf., vol. 6, no. 1, pp. 107–116, 2023.
[21] E. Yuninsar et al., “Analisis Perbandingan Metode Convolutional Neural Network ( CNN ) Dan Artificial Neural Network ( ANN ),” no. 03, pp. 321–333, 2024.
[22] S. A. Hakim et al., “Klasifikasi Citra Generasi Artificial Intelligence Menggunakan Metode Fine Tuning Pada Residual Network Ai Generated Image Classification Using Fine Tuning on Residual Network,” vol. 11, no. 3, pp. 655–666, 2024, doi: 10.25126/jtiik.938118.
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