KLASIFIKASI PENYAKIT TANAMAN NILAM BERDASARKAN CITRA DAUN MENGGUNAKAN GLCM DAN SVM
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Abstract
This study presents a classification model for detecting diseases in patchouli (Pogostemon cablin Benth) leaves using image processing techniques. The method combines Grey Level Co-occurrence Matrix (GLCM) for texture feature extraction and Support Vector Machine (SVM) for classification, optimised using the Particle Swarm Optimisation (PSO) algorithm. A total of 2,080 leaf images were collected and categorized into four classes: healthy, leaf spot, yellowing, and mosaic. Each image was augmented and converted to grayscale to enhance the dataset and reduce computational complexity. Four GLCM features—contrast, correlation, energy, and homogeneity—were extracted to represent leaf textures. The classification model achieved an accuracy of 89.74% using SVM alone, and improved to 97.12% when optimized with PSO. The results indicate that the integration of GLCM, SVM, and PSO provides an effective and accurate solution for early detection of patchouli leaf diseases, potentially supporting farmers in decision-making and improving crop productivity and quality.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Sarina, Bakti, R. Y., Muhammad Faisal, Muhammad Syafaat, Syamsuri, A. M., AM Hayat, M., & Anas, A. L. (2025). KLASIFIKASI PENYAKIT TANAMAN NILAM BERDASARKAN CITRA DAUN MENGGUNAKAN GLCM DAN SVM. Jurnal Informatika Progres, 17(2), 12-22. https://doi.org/10.56708/progres.v17i2.469
References
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[2] Z. Zulfadli, N. I. Wasistha, H. Oktarina, K. Khairan, and R. Sriwati, “Pathogens causing wilt diseases in patchouli plant ( Pogostemon cablin Benth.): A review on symptoms, bioecology, and management,” IOP Conf. Ser. Earth Environ. Sci., vol. 1183, no. 1, 2023, doi: 10.1088/1755-1315/1183/1/012027.
[3] G. Gu et al., “Continuous cropping of Patchouli alters soil physiochemical properties and rhizosphere microecology revealed by metagenomic sequencing,” Front. Microbiol., vol. 15, 2024, doi: 10.3389/fmicb.2024.1482904.
[4] S. Suharyana, F. Anwar, A. C. Dewi, M. Yunianto, U. Salamah, and R. Chai, “Pneumonia Classification Based on GLCM Features Extraction using K-Nearest Neighbor,” Indones. J. Appl. Phys., vol. 13, no. 2, p. 325, 2023, doi: 10.13057/ijap.v13i2.77120.
[5] E. Nahak, R. P. Putra, and F. Marisa, “Klasifikasi Penyakit Pada Tanaman Apel Melalui Citra Daun Menggunakan Metode Multiclass Support Vector Machine,” J. Tek. Inform. Sist. Inf., vol. 11, no. 3, pp. 401–408, 2024, [Online]. Available: http://jurnal.mdp.ac.id
[6] Irene Oktaviani Duka, Huan Arthur Ado, and Yampi R.Kaesmetan, “Identifikasi Penyakit Tanaman Citra Daun Cabe Menggunakan Gray Level Co-Occurrencce Matrix Dan Support Vector Machine,” Neptunus J. Ilmu Komput. Dan Teknol. Inf., vol. 2, no. 2, pp. 41–52, 2024, doi: 10.61132/neptunus.v2i2.86.
[7] A. Tabbakh and S. S. Barpanda, “Evaluation of Machine Learning Models for Plant Disease Classification Using Modified GLCM and Wavelet Based Statistical Features,” Trait. du Signal, vol. 39, no. 6, pp. 1893–1905, 2022, doi: 10.18280/ts.390602.
[8] M. Jamjoom, A. Elhadad, H. Abulkasim, and S. Abbas, “Plant Leaf Diseases Classification Using Improved K-Means Clustering and SVM Algorithm for Segmentation,” Comput. Mater. Contin., vol. 76, no. 1, pp. 367–382, 2023, doi: 10.32604/cmc.2023.037310.
[9] P. M. Dinesh, “Texture image classification using hybridization of glcm and glwm features,” Int. J. Sci. Technol. Res., vol. 9, no. 4, pp. 3216–3219, 2020.
[10] L. lin Zhang, D. jian He, and H. bo Song, “Automatic tracking of multi-fruit targets with oscillation under natural growth conditions,” Inf. Process. Agric., vol. 7, no. 4, pp. 546–555, 2020, doi: 10.1016/j.inpa.2019.11.004.
[11] R. Andrian, D. Maharani, M. A. Muhammad, and A. Junaidi, “Butterfly identification using gray level co-occurrence matrix (Glcm) extraction feature and k-nearest neighbor (knn) classification,” Regist. J. Ilm. Teknol. Sist. Inf., vol. 6, no. 1, pp. 11–21, 2020, doi: 10.26594/register.v6i1.1602.
[12] E. D. Hartono and B. Hardiansyah, “Analisis Intensitas Cahaya Lampu Pijar dengan Menerapkan Metode Gray Level Co-occurence Matrik,” J. Sist. Komput. dan Inform., vol. 4, no. 2, p. 407, 2022, doi: 10.30865/json.v4i2.5366.
[13] R. Cicchi, E. Baria, M. Mari, G. Filippidis, and D. Chorvat, “Extraction of collagen morphological features from second-harmonic generation microscopy images via GLCM and CT analyses: A cross-laboratory study,” J. Biophotonics, vol. 17, no. 8, pp. 1–11, 2024, doi: 10.1002/jbio.202400090.
[14] M. Kabir, F. Unal, T. C. Akinci, A. A. Martinez-Morales, and S. Ekici, “Revealing GLCM Metric Variations across a Plant Disease Dataset: A Comprehensive Examination and Future Prospects for Enhanced Deep Learning Applications,” Electron., vol. 13, no. 12, 2024, doi: 10.3390/electronics13122299.
[15] H. H. Fauziah, D. R. Ningtias, B. Wahyudi, and ..., “Identification of lung cancer using gray level co-occurrence matrix (GLCM) and artificial neural network with backpropagation algorithm,” J. Soft …, pp. 51–61, 2025, [Online]. Available: https://shmpublisher.com/index.php/joscex/article/view/543%0Ahttps://shmpublisher.com/index.php/joscex/article/download/543/303
[16] F. Putrawansyah, “Penerapan Metode Support Vector Machine Terhadap Klasifikasi Jenis Jambu Biji,” JIKO (Jurnal Inform. dan Komputer), vol. 8, no. 1, p. 193, 2024, doi: 10.26798/jiko.v8i1.988.
[17] D. Udjulawa, “Klasifikasi Tanaman Hias Berdasarkan Tekstur Daun Menggunakan Metode Svm Dan Fitur Glcm,” Klik - J. Ilmu Komput., vol. 3, no. 2, pp. 121–127, 2023, doi: 10.56869/klik.v3i2.418.
[18] S. Rabbani, D. Safitri, N. Rahmadhani, A. A. F. Sani, and M. K. Anam, “Perbandingan Evaluasi Kernel SVM untuk Klasifikasi Sentimen dalam Analisis Kenaikan Harga BBM,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 3, no. 2, pp. 153–160, 2023, doi: 10.57152/malcom.v3i2.897.
[19] S. Rahayu and Y. Yamasari, “Klasifikasi Penyakit Stroke dengan Metode Support Vector Machine (SVM),” J. Informatics Comput. Sci., vol. 5, no. 03, pp. 440–446, 2024, doi: 10.26740/jinacs.v5n03.p440-446.
[20] A. K. Balyan et al., “A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method,” Sensors, vol. 22, no. 16, pp. 1–20, 2022, doi: 10.3390/s22165986.
[21] M. I. Malik, “Optimization Design of Single-Tuned Passive Filter Using Particle Swarm Optimization Algorithm in Industry,” J. Energy Electr. Eng., vol. 6, no. 2, pp. 137–