Penerapan Deep Learning untuk Klasifikasi Penyakit
DOI:
https://doi.org/10.29407/b125z445Keywords:
citra daun, CNN, deep learning, klasifikasi, penyakit tomatAbstract
Tanaman tomat ( Solanum lycopersicum ) merupakan komoditas hortikultura bernilai ekonomi tinggi, namun produktivitasnya sering menurun akibat serangan berbagai penyakit daun. Proses identifikasi penyakit daun tomat secara manual memerlukan keahlian khusus dan waktu yang relatif lama, sehingga penanganan sering terlambat dan kurang efektif. Oleh karena itu, diperlukan solusi berbasis teknologi yang mampu mendeteksi penyakit daun tomat secara cepat dan akurat. Penelitian ini bertujuan mengembangkan sistem klasifikasi penyakit daun tomat menggunakan metode Deep Learning dengan arsitektur Convolutional Neural Network (CNN) yang diintegrasikan ke dalam aplikasi web. Citra daun tomat diproses melalui tahapan pra-pemrosesan berupa resize, normalisasi, serta data augmentation untuk meningkatkan kemampuan generalisasi model. CNN digunakan untuk mengekstraksi fitur visual dan mengklasifikasikan berbagai jenis penyakit daun tomat secara otomatis. Evaluasi performa model dilakukan menggunakan confusion matrix dan metrik akurasi, precision, recall, serta F1-score. Hasil penelitian menunjukkan bahwa model CNN mampu mengklasifikasikan penyakit daun tomat dengan performa yang baik dan konsisten. Sistem yang dikembangkan dapat digunakan sebagai alat bantu deteksi dini penyakit daun tomat, sehingga diharapkan mampu membantu petani dalam menentukan tindakan penanganan yang tepat serta meningkatkan produktivitas dan kualitas hasil panen tomat.
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[1] A. Jupri, S. Suripto, S. Billah, T. Rozi, E. S. Prasedya, and N. Hidayah, “Pemanfaatan Komoditas Tomat (Lycopersicum esculentum Mill) Guna Menciptakan Nilai Tambah Dengan Melakukan Sosialisasi Pembuatan Manisan Tomat Di Desa Paok Pampang Kecamatan Sukamulia Lombok Timur,” Jurnal Gema Ngabdi, vol. 5, no. 2, pp. 236–240, Jul. 2023, doi: 10.29303/jgn.v5i2.381.
[2] A. Maysela, N. Rohma, and N. Penulis:, “Diagnosa Penyakit Tanaman Tomat pada Citra Daun Menggunakan Metode Convolutional Neural Network (CNN),” JIMU, 2024.
[3] D. R. Sihotang, D. Syafitri, D. C. Octaviana, P. A. Septianingrum, and A. Asy-Syfaiyyah, “Identification of Pests and Diseases in Tomato Plants in Jeprono Village, Karangbangun District, Karanganyar Regency,” Jurnal Biologi Tropis, vol. 25, no. 1, pp. 381–393, Feb. 2025, doi: 10.29303/jbt.v25i1.8229.
[4] A. R. Wang and N. H. Shabrina, “A deep learning-based mobile app system for visual identification of tomato plant disease,” International Journal of Electrical and Computer Engineering, vol. 13, no. 6, pp. 6992–7004, Dec. 2023, doi: 10.11591/ijece.v13i6.pp6992-7004.
[5] B. Kommey, E. Tamakloe, D. Opoku, T. Crispin, and J. Danquah, “Disease Detection in Tropical Tomato Leaves via Machine Learning Models,” Jurnal ELTIKOM, vol. 8, no. 2, pp. 179–191, Dec. 2024, doi: 10.31961/eltikom.v8i2.1340.
[6] J. Lu, L. Tan, and H. Jiang, “Review on convolutional neural network (CNN) applied to plant leaf disease classification,” Aug. 01, 2021, MDPI AG. doi: 10.3390/agriculture11080707.
[7] S. M. Sivalingam and L. D. Badabagni, “Encouraging hygiene permanence in tomato leaf and applying machine learning techniques,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 33, no. 1, pp. 343–349, Jan. 2024, doi: 10.11591/ijeecs.v33.i1.pp343-349.
[8] Y. H. Natbais and A. B. S. Umbu, “Aplikasi Deteksi Penyakit pada Daun Tomat Berbasis Android Menggunakan Model Terlatih Tensorflow Lite,” TEKNOTAN, vol. 17, no. 2, p. 83, Aug. 2023, doi: 10.24198/jt.vol17n2.1.
[9] F. A. Mohammed, K. K. Tune, B. G. Assefa, M. Jett, and S. Muhie, “Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature,” Mar. 01, 2024, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/make6010033.
[10] C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J Big Data, vol. 6, no. 1, Dec. 2019, doi: 10.1186/s40537-019-0197-0.
[11] L. Mohammadpour, T. C. Ling, C. S. Liew, and A. Aryanfar, “A Survey of CNN-Based Network Intrusion Detection,” Aug. 01, 2022, MDPI. doi: 10.3390/app12168162.
[12] X. Xu et al., “Crack Detection and Comparison Study Based on Faster R-CNN and Mask R-CNN,” Sensors, vol. 22, no. 3, Feb. 2022, doi: 10.3390/s22031215.
[13] S. Sathyanarayanan, “Confusion Matrix-Based Performance Evaluation Metrics,” African Journal of Biomedical Research, pp. 4023–4031, Nov. 2024, doi: 10.53555/ajbr.v27i4s.4345.
[14] O. Rainio, J. Teuho, and R. Klén, “Evaluation metrics and statistical tests for machine learning,” Sci Rep, vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-56706-x.
[15] “Convolutional neural network.”
[16] S. Sathyanarayanan, “Confusion Matrix-Based Performance Evaluation Metrics,” African Journal of Biomedical Research, pp. 4023–4031, Nov. 2024, doi: 10.53555/ajbr.v27i4s.4345.
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