ANALISIS KINERJA METODE GLCM DAN LS-SVM DALAM KLASIFIKASI CITRA SAMPAH ORGANIK DAN ANORGANIK
DOI:
https://doi.org/10.35316/jimi.v10i2.105-111Keywords:
Pengolahan Citra Digital, waste classification, GLCM, LS-SVM, Machine Learning, Flask Web ApplicationAbstract
Waste management, particularly in distinguishing organic and inorganic types, remains a major environmental challenge. Manual sorting processes are inefficient and prone to errors. This study aims to develop an automated waste image classification system using a combination of Gray Level Co-occurrence Matrix (GLCM) and Least Squares Support Vector Machine (LS-SVM). A total of 1,060 images were used, divided equally between organic and inorganic categories. Texture features such as contrast, correlation, energy, and homogeneity were extracted using GLCM and combined with mean RGB color features. The LS-SVM model with the Radial Basis Function (RBF) kernel achieved an accuracy of 87 percent, outperforming conventional SVM. The model’s effectiveness aligns with previous studies that used SVM-based waste classification and texture feature enhancement with GLCM descriptors. The model was implemented using a Flask web application for real-time predictions.
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[1] F. Nisa, T. Wulandari, and N. Hasanah, “Implementasi metode GLCM untuk klasifikasi citra tekstur pada pengolahan limbah,” Jurnal Teknologi dan Sistem Komputer, vol. 10, no. 3, pp. 211–218, 2022
[2] M. A. Talib, R. Rahman, and A. Setiawan, “Analisis parameter GLCM untuk pengenalan pola citra tekstur,” Jurnal Rekayasa Sistem dan Teknologi Informasi, vol. 12, no. 1, pp. 33–41, 2024
[3] D. Lapendy, P. Siregar, and M. Rinaldi, “Klasifikasi rasa jeruk siam berdasarkan warna dan tekstur citra digital,” Jurnal Sains Komputer dan Informatika, vol. 9, no. 2, pp. 58–66, 2023
[4] H. Wong, “Performance of LS-SVM on waste image classification,” Indonesian Journal of Computer Science, vol. 8, no. 2, pp. 145–152, 2022
[5] F. Prayoga, A. Anwar, and R. Yuliani, “Evaluasi metrik akurasi dan recall pada model CNN untuk klasifikasi citra batik,” Jurnal Ilmiah Informatika dan Komputer, vol. 10, no. 1, pp. 12–21, 2023
[6] M. Abdillah, A. Wijayanti, and D. Sutrisno, “Detektif Sampah: Klasifikasi jenis sampah organik dan anorganik berbasis website dengan YOLOv5,” Jurnal Sistem Informasi dan Teknologi, vol. 10, no. 1, pp. 34–42, 2024
[7] H. Wijayanto and B. Susetyo, “Implementasi Flask Framework pada pembangunan aplikasi sistem informasi helpdesk,” Jurnal Teknologi dan Sistem Informasi, vol. 8, no. 2, pp. 67–74, 2022
[8] A. P. Puspaningrum, H. S. Widodo, and A. F. Rochim, “Waste Classification Using Support Vector Machine with SIFT-PCA Feature Extraction,” in Proceedings of the 4th International Conference on Informatics and Computational Sciences (ICICoS), Semarang, Indonesia, pp. 1–6, IEEE, 2020, doi: 10.1109/ICICoS51170.2020.9298982, [Online].
Tersedia: https://ieeexplore.ieee.org/document/9298982 [Diakses 7 Oktober 2025].
[9] C. M. Jackson, M. W. Smith, and E. H. Kearney, “Feature Extraction and Classification of Canopy Gaps Using GLCM- and MLBP-Based Rotation-Invariant Feature Descriptors Derived from WorldView-3 Imagery,” Geomatics, vol. 3, no. 1, pp. 1–14, 2023, doi: 10.3390/geomatics3010014
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