Sistem Pengenalan Jenis Kanker Melanoma pada Citra MenggunakanGray Level Co-occurrence Matrices (GLCM) dan K-Nearest Neighbor (KNN) Classifier

YOULLIA INDRAWATY NURHASANAH; IRMA AMELIA DEWI; FEVLY PALLAR

doi:10.26760/mindjournal.v5i1.66-80

Sistem Pengenalan Jenis Kanker Melanoma pada Citra MenggunakanGray Level Co-occurrence Matrices (GLCM) dan K-Nearest Neighbor (KNN) Classifier

YOULLIA INDRAWATY NURHASANAH, IRMA AMELIA DEWI, FEVLY PALLAR

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Abstrak

Melanoma dikategorikan sebagai bentuk kanker kulit yang paling berbahaya menurut skincancer.org. Kanker kulit ini bertumbuh dan berkembang oleh kerusakan DNA pada sel-sel kulit yang umumnya disebabkan oleh radiasi ultraviolet dari matahari. Pada penelitian ini Â dibuatkan suatu sistem yangÂ dapat membantu pihak medis untuk memprediksi suatu tipe atau jenis dari suatu kanker melanoma dengan proses antara lain, optimalisasi postprocessing melalui morphological closing, pembentukan matriks-matriks gray level co-occurrence (GLCM) untuk pengekstraksian fitur-fitur tekstur statistika dan K-Nearest Neighbor (KNN) sebagai metode klasifikasinya. Hasil dari pengujian menunjukan bahwa ekstraksi ciri tekstur statistika bermanfaat dalam pengenalan kanker ini dimana diperoleh hasil akurasi mencapai 93.33% oleh classfier pada kategori pengujian positif melanoma dan 86.66 % pada kategori kelas melanoma.

Kata kunci: Melanoma, GLCM, K-Nearest Neighbor, Otsu Thresholding

Abstract

Melanoma is categorized as the most dangerous form of skin cancer, according to skincancer.org. This skin cancer grows and develops due to DNA damage to skin cells which is generally caused by ultraviolet radiation. In this study, a system was created to help medical parties predict a type or type of melanoma cancer. This system was performed with morphological closing processes, the formation of gray level co-occurrence (GLCM) matrices for extraction of features of statistical textures, and K-Nearest Neighbor (KNN) as a classification method. The test results showed that the system recognized this cancer with an accuracy of 93.33% for the positive image of melanoma and 86.66% for the melanoma class category.

Keywords: Melanoma, GLCM, K-Nearest Neighbor, Otsu Thresholding

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Referensi

DAFTAR RUJUKAN

Albregtsen, F. (2018). Statistical Texture Measures Computed from Gray Level Coocurrence Matrices. Oslo: Image Processing Laboratory, Department of Informatics, University of Oslo.

Almomani, A., Alweshah, M., Alkhalaileh, S., Refai, M., & Qashi, R. (2019). Metaheuristic Algorithms-based Feature Selection Approach for Intrusion Detection. Machine Learning for Computer and Cyber Security, 184-208.

Ansari, U. B., & Sarode, T. (2017). Skin Cancer Detection Using Image Processing. International Research Journal of Engineering and Technology (IRJET), 2875-2881.

Barmawi, M., Zulkarnain, A., & Hidayat, A. (2017). Implementasi Algoritma GLCM Dan MED pada Aplikasi Pendeteksi Kolesterol Melalui Iris Mata. MIND (Multimedia, Artificial Intelligence, Networking, and Database) Journal, 2(2), 23-42.

Cheng, D., Zhang, S., Deng, Z., Zhu, Y., & Zong, M. (2014). kNN Algorithm with Data-Driven k Value. Conference: International Conference on Advanced Data Mining and Applications. China.

Damayana, I., Atmaja, R. D., & Fauzi, H. (2016). Deteksi kanker kulit melanoma berbasis pengolahan citra menggunakan wevelet transform. e-Proceeding of Engineering, (pp. 4718-4723).

Gonzalez, R., Woods, R., & Eddins, L. (2009). Digital Image Processing Using MATLAB, 2nd Edition. Knoxville: Gatesmark, LLC.

Gurkirat Kaur, K. J. (2015). Automatic Detection and Segmentation of Skin Melanoma Images- An Introduction. International Journal of Emerging Research in Management &Technology, 120-123.

Mesquita, D., Corona, F., Corona, F., Souza, A., & Nobre, J. (2019). Gaussian kernels for incomplete data. Applied Soft Computing 77, 1-22.

Munir, R. (2004). Pengolahan CItra Digital. Bandung: Informatika Bandung.

Rahman, S., Rahman, M., Al-Wadud, M., Al-Quaderi, G., & Shoyaib, M. (2016). An adaptive gamma correction for image enhancement. EURASIP Journal on Image and Video Processing, 1-13.

Sebastian , B., Unnikrishnan, A., & Balakrishnan, K. (2012). Grey Level Co-Occurrence Matrices:Generalisation And Some New Features. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), 2(2), 151-157.

Sheha, M. A., Mabrouk, M. S., & Sharawy, A. (2012). Automatic Detection of Melanoma Skin Cancer using Texture Analysis. International Journal of Computer Applications, 22-26.

Shi, Y., & Judd, M. (2013). Finding Nearest Neighbors for Multi-Dimensional Data. 5th International Conference on Advances in Databases, Knowledge, and Data Applications (pp. 52-). Seville, Spain: International Academy, Research, and Industry Association ( IARIA ).

Shidnal, S. (2014). A texture feature extraction of crop field images using GLCM approach. International Journal of Science Engineering and Advance Technology, 1006-1010.

Solomon, C., & Breckon, T. (2011). Fundamentals of Digital Image Processing. Chichester West Sussex: John Wiley & Sons Ltd.

Widodo, R., Widodo, A., & Supriyanto, A. (2018). Pemanfaatan Ciri Gray Level Co-Occurrence Matrix (GLCM) Citra Buah Jeruk Keprok (Citrus reticulata Blanco) untuk Klasifikasi Mutu. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2(11), 5769-5776.

Wu, J., Cai, Z., & Gao, Z. (2010). Dynamic K-Nearest-Neighbor with Distance and attribute weighted for classification. 2010 International Conference on Electronics and Information Engineering. Kyoto, Japan: IEEE.

Zhang, S., Li, X., Zong, M., Zhu, X., & Cheng, D. (2017). Learning k for kNN Classification. ACM Transactions on Intelligent Systems and Technology, 8(3), 1-19.

DOI: https://doi.org/10.26760/mindjournal.v5i1.66-80