Feature Extraction dengan Gray Level Co-Occurrence Matrix Warna Alami dari Tanaman Ketapang berbasis Geolokasi
Abstract
Ekstraksi fitur, juga dikenal sebagai ekstraksi fitur, adalah proses mengubah data mentah menjadi representasi yang lebih ringkas dan berguna yang dapat digunakan untuk analisis atau pemodelan lebih lanjut. Ini dilakukan pada tahap preprocessing sebelum masuk ke tahap analisis atau pemodelan. Pewarnaan alami dari berbagai tanaman yang menghasilkan warna tanin setelah pencelupan dikumpulkan menjadi satu alur gradasi warna yang berkaitan dengan susunan derajat atau peningkatan, peralihan warna dari satu warna ke warna lain. Variasi dalam alur ini dipengaruhi oleh jenis tanaman, lama pencelupan, jenis fiksasi yang digunakan, jenis kain, dan lokasi tanaman yang dijadikan sampel.Metode ekstraksi fitur dengan Matriks Co-Occurrence Level Gray (GLCM) digunakan untuk mengidentifikasi gradasi warna yang dihasilkan oleh pewarnaan pada daun Ketapang. Proses ekstraksi fitur tekstur dari gambar tanaman Ketapang menggunakan matriks GLCM digunakan untuk menganalisis dan memahami pola warna alami tanaman Ketapang di berbagai lokasi geografis. Untuk menggambarkan dan mengukur pola warna alami tanaman Ketapang, Gray Level Co-occurrence Matrix (GLCM) adalah representasi statistik dari distribusi spasial intensitas piksel dalam citra, yang mengukur frekuensi kemunculan pasangan intensitas piksel yang berdekatan dan memberikan informasi tentang tekstur citra
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Aouat, S., Ait-hammi, I., & Hamouchene, I. (2021). A new approach for texture segmentation based on the Gray Level Co-occurrence Matrix. Multimedia Tools and Applications, 80(16). https://doi.org/10.1007/s11042-021-10634-4
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Cahyono, B. E., Nugroho, A. T., & Maulinida, I. W. (2023). Klasifikasi Jenis Biji Kopi dengan Menggunakan Metode Gray Level Co-occurrence Matrix (GLCM). TEKNOTAN, 16(3). https://doi.org/10.24198/jt.vol16n3.9
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Ehsanirad, A., & H, S. K. Y. (2010). Leaf recognition for plant classification using GLCM and PCA methods. Oriental Journal of Computer Science & Technology, 3(1).
Extraction of Tannin From Ketapang Leaves (Terminalia catappa Linn). (2020). https://doi.org/10.11594/nstp.2020.0530
Faisal, R. M., & Chafidz, A. (2019). Extraction of Natural Dye from Ketapang Leaf (Terminalia catappa) for Coloring Textile Materials. IOP Conference Series: Materials Science and Engineering, 543(1). https://doi.org/10.1088/1757-899X/543/1/012074
Gebejes, a, Master, E. M., & Samples, a. (2013). Texture Characterization based on Grey-Level Co-occurrence Matrix. Conference of Informatics and Management Sciences.
Guo, Q., Su, Y., & Hu, T. (2023). Data Preprocessing and Feature Extraction. In LiDAR Principles, Processing and Applications in Forest Ecology. https://doi.org/10.1016/b978-0-12-823894-3.00005-0
Hung, H. D., Tien, D. D., Ngoan, N. T., Duong, B. T., Viet, D. Q., Dien, P. G., & Anh, B. K. (2022). CHEMICAL CONSTITUENTS FROM THE LEAVES OF TERMINALIA CATAPPA L. (COMBRETACEAE). Vietnam Journal of Science and Technology, 60(4). https://doi.org/10.15625/2525-2518/15972
Jansen, S. A., Malaty, M., Nwabara, S., Johnson, E., Ghabbour, E., Davies, G., & Varnum, J. M. (1996). Structural modeling in humic acids. Materials Science and Engineering C, 4(3). https://doi.org/10.1016/S0928-4931(96)00151-8
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Krisnawati, M., Cahyani, I. W. N., Paramita, O., & Kusumastuti, A. (2022). Textile natural dye powder of Terminalia catappa leaves. IOP Conference Series: Earth and Environmental Science, 969(1). https://doi.org/10.1088/1755-1315/969/1/012038
Kumarmath, P. S., Kawatal, A., & Nimbargi, K. (2022). A Review On Extraction Of Dye From Terminalia catappa Hull: A Substitute To Synthetic Dyes. Journal of Emerging Technologies and Innovative Research (JETIR), 9(2).
López-Hernández, E., Ponce-Alquicira, E., Cruz-Sosa, F., & Guerrero-Legarreta, I. (2001). Characterization and stability of pigments extracted from Terminalia catappa leaves. Journal of Food Science, 66(6). https://doi.org/10.1111/j.1365-2621.2001.tb15182.x
Madhavan, K., Rukayadi, Y., & Mutalib, N. A. A. (2023). Phytochemical Constituents and Toxicity Analysis of Ethanolic Ketapang (Terminalia catappa L.) Leaf Extract. Malaysian Applied Biology, 52(3). https://doi.org/10.55230/mabjournal.v52i3.2685
Mohanaiah, P., Sathyanarayana, P., & Gurukumar, L. (2013). Image Texture Feature Extraction Using GLCM Approach. International Journal of Scientific & Research Publication, 3(5).
Mutlag, W. K., Ali, S. K., Aydam, Z. M., & Taher, B. H. (2020). Feature Extraction Methods: A Review. Journal of Physics: Conference Series, 1591(1). https://doi.org/10.1088/1742-6596/1591/1/012028
P.S, S. K., & V.S, D. (2016). Extraction of Texture Features using GLCM and Shape Features using Connected Regions. International Journal of Engineering and Technology, 8(6). https://doi.org/10.21817/ijet/2016/v8i6/160806254
Purnama, H., Eriani, W., & Hidayati, N. (2019). Natural dye extraction from tropical almond (Terminalia catappa Linn) leaves and its characterization. AIP Conference Proceedings, 2114. https://doi.org/10.1063/1.5112470
Rosiva Srg, S. A., Zarlis, M., & Wanayumini, W. (2022). Identifikasi Citra Daun dengan GLCM (Gray Level Co-Occurence) dan K-NN (K-Nearest Neighbor). MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(2). https://doi.org/10.30812/matrik.v21i2.1572
Suhendri, S., Muhammad Muharam, F., & Aelani, K. (2017). Implementasi Support Vector Machine (SVM) Untuk Klasifikasi Jenis Daun Mangga Menggunakan Metode Gray Level Co-Occurrence Matrix. Kopertip : Jurnal Ilmiah Manajemen Informatika Dan Komputer, 1(3). https://doi.org/10.32485/kopertip.v1i03.22
Suhendri, S., & Rahayu, P. (2019). Metode Grayscale Co-occurrence Matrix (GLCM) Untuk Klasifikasi Jenis Daun Jambu Air Menggunakan Algoritma Neural Network. Journal of Information Technology, 1(1). https://doi.org/10.47292/joint.v1i1.4
Suresh, A., & Shunmuganathan, K. L. (2012). Image texture classification using gray level co-occurrence matrix based statistical features. European Journal of Scientific Research, 75(4).
Toshikj, E., & Prangoski, B. (2022). Grey level co-occurrence matrix (GLCM) for textile print analysis. Tekstilna Industrija, 70(4). https://doi.org/10.5937/tekstind2204034t
Wang, M., Wang, J., & Wang, Y. S. (2016). Multi-scale algorithm of texture feature extraction based on gray-level co-occurrence matrix. Chinese Journal of Liquid Crystals and Displays, 31(10). https://doi.org/10.3788/YJYXS20163110.0967
Zheng, W. (2023). Current Technologies and Applications of Digital Image Processing. Journal of Biomedical and Sustainable Healthcare Applications. https://doi.org/10.53759/0088/jbsha202303002
Published
2024-07-31
How to Cite
Ningsih, D. H. U., Zuliarso, E., Radyanto, M. R., & Santoso, D. B. (2024). Feature Extraction dengan Gray Level Co-Occurrence Matrix Warna Alami dari Tanaman Ketapang berbasis Geolokasi. Dinamik, 29(2), 153-162. https://doi.org/10.35315/dinamik.v29i2.9851
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