PEMANFAATAN METODE GLCM, PCA, DAN SVM UNTUK KLASIFIKASI MUTU WORTEL
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Abstract
Kualitas wortel merupakan faktor krusial dalam industri pertanian, namun metode penilaian manual yang umum digunakan bersifat subjektif dan tidak konsisten. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi mutu wortel otomatis berbasis citra digital untuk mengatasi masalah tersebut. Metode yang digunakan mengintegrasikan Gray Level Co-occurrence Matrix (GLCM) untuk ekstraksi fitur tekstur, Principal Component Analysis (PCA) untuk reduksi dimensi, dan Support Vector Machine (SVM) dengan kernel RBF untuk klasifikasi. Sistem diuji menggunakan dataset 409 citra yang terbagi dalam tiga kelas: segar, kurang segar, dan busuk. Hasil pengujian menunjukkan sistem mencapai akurasi keseluruhan sebesar 90,54%. Model menunjukkan performa sangat baik pada kelas segar (akurasi 96,6%) dan busuk (akurasi 93,5%), namun performanya lebih rendah pada kelas kurang segar (akurasi 71,4%) yang bersifat transisi. Kombinasi metode yang diusulkan terbukti efektif dan memiliki potensi untuk diterapkan dalam sistem sortir otomatis di industri pertanian.
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References
[1] N. O. Boadi, M. Badu, N. K. Kortei, S. A. Saah, B. Annor, M. B. Bensah , H. Okyere and A. Fiebor, "Nutritional composition and antioxidant properties of three varieties of carrot (Daucus carota)," Scientific African, vol. 12, pp. 1-8, 2021.
[2] B. N. Mirani, S. H. Chattha, S. A. Soomro, B. Wagan, I. A. Dahri, Z. A. Khan, G. Zahid and B. M. Ansari, "Effect of Post-Harvest Treatments on Quality Characteristics of Carrots During Storage," RADS Journal of Biological Research & Applied Sciences, vol. 13, no. 2, pp. 45-51, 2022.
[3] A. Jahanbakhshi and K. Kheiralipour, "Evaluation of image processing technique and discriminant analysis methods in postharvest processing of carrot fruit," Food Science & Nutrition, vol. 8, no. 3, pp. 3346-3352, 2020.
[4] G. Prasad, V. S. Gaddale, R. C. Kamath, V. J. Shekaranaik and S. P. Pai, "A Study of Dimensionality Reduction in GLCM Feature-Based Classification of Machined Surface Images," Arabian Journal for Science and Engineering, vol. 49, pp. 1531-1553, 2024.
[5] K.-L. Du, B. Jiang, J. Lu, J. Hua and M. S. Swamy, "Exploring Kernel Machines and Support Vector Machines: Principles, Techniques, and Future Directions," Mathematics, vol. 12, no. 24, pp. 1-58, 2024.
[6] A. T. W. Almais, A. Susilo, A. Naba, M. Sarosa, A. M. Juwono, C. Crysdian, M. A. Muslim and H. Wicaksono, "Characterization of Structural Building Damage in Post-Disaster Using GLCM-PCA Analysis Integration," IEEE Access, vol. 12, pp. 146190-146201, 2024.
[7] P. and D. Kumar, "Feature Extraction and Selection of kidney Ultrasound Images Using GLCM and PCA," Procedia Computer Science, pp. 1722-1731, 2020.
[8] S. A. R. Srg, M. Zarlis and W. , "Klasifikasi Citra Daun dengan GLCM (Gray Level Co-Occurence) dan K-NN (K-Nearest Neighbor)," Matrik: Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer, vol. 2, no. 2, pp. 477-486, 2022.
[9] M. Abuhussein and A. Robinson, "Obscurant Segmentation in Long Wave Infrared Images Using GLCM Textures," Journal of Imaging, vol. 8, no. 10, 2022.
[10] M. Yunus, "Feature Extraction : Gray Level Co-occurrence Matrix (GLCM)," Medium, 16 July 2020. [Online]. Available: https://yunusmuhammad007.medium.com/feature-extraction-gray-level-co-occurrence-matrix-glcm-10c45b6d46a1. [Accessed 18 June 2025].
[11] S. Rathod, S. Banda, N. Bandumula, A. Kumar, L. Prasanna, P. Jeyakumar, Waris Amtul, P. Muthuraman and R. M. Sundaram, Statistical Procedures for Analyzing Agricultural Data using R, Telangana: ICAR- Indian Institute of Rice Research, 2023.
[12] D. Hediyati and I. M. Suartana, "Penerapan Principal Component Analysis (PCA) Untuk Reduksi Dimensi Pada Proses Clustering Data Produksi Pertanian Di Kabupaten Bojonegoro," JIEET (Journal of Information Engineering and Educational Technology), vol. 5, no. 2, pp. 49-54, 2021.
[13] S. Muthukrishnan, H. Krishnaswamy, S. Thanikodi, D. Sundaresan and V. Venkatraman, "Support Vector Machine for Modelling and Simulation of Heat Exchangers," Thermal Science, vol. 24, pp. 499-503, 2020.
[14] S. Swaminathan and B. R. Tantri, "Confusion Matrix-Based Performance Evaluation Metric," African Journal of Biomedical Research, vol. 27, pp. 4023-4031, 2024.