Perbandingan Hybrid Genetic K-Means++ dan Hybrid Genetic K-Medoid untuk Klasterisasi Dataset EEG Eyestate
Article Sidebar
Published:
Oct 2, 2020
Keywords:
EEG Eyestate Dataset GA K-Means K-Medoid Cluster
Main Article Content
Abstract
K-Means++ and K-Medoids are data clustering methods. The data cluster speed is determined by the iteration value, the lower the iteration value, the faster the data clustering is done. Data clustering performance can be optimized to get more optimal clustering results. One algorithm that can optimize cluster speed is Genetic Algorithm (GA). The dataset used in the study is a dataset of EEG Eyestate. The optimization results before hybrid GA on K-Means++ are the iteration average values is 11.6 to 5,15, and in K-Medoid are the iteration average values decreased from 5.9 to 5.2. Based on the comparison of GA K-Means++ and GA K-Medoids iterations, it can be concluded that GA - K-Means++ better
Downloads
Download data is not yet available.
Article Details
How to Cite
Al Rivan, M. E., Gandi, G. P., & Lukman, F. N. (2020). Perbandingan Hybrid Genetic K-Means++ dan Hybrid Genetic K-Medoid untuk Klasterisasi Dataset EEG Eyestate. PETIR, 14(1), 103–113. https://doi.org/10.33322/petir.v14i1.953
Section
Articles
References
[1] Yong-Guo Liu, Ke-Fei Chen, and Xue-Ming Li, “A hybrid genetic based clustering algorithm,” Proc. Third Int. Conf. Mach. Leaming Cybern., no. August, pp. 1677–1682, 2005, doi: 10.1109/icmlc.2004.1382045.
[2] A. Al Malki, M. M. Rizk, M. A. El-Shorbagy, and A. A. Mousa, “Hybrid Genetic Algorithm with K-Means for Clustering Problems,” Open J. Optim., vol. 05, no. 02, pp. 71–83, 2016, doi: 10.4236/ojop.2016.52009.
[3] G. Yamini and B. R. Devi, “A New Hybrid Clustering Technique Based On Mini-batch K-means And K-means++ For Analysing Big Data.,” Int. J. Recent Res. Asp., no. April, pp. 203–208, 2018.
[4] W. Sheng and X. Liu, “A Genetic K-medoids Clustering Algorithm,” J. Heuristics, vol. 12, no. 6, pp. 447–466, 2006.
[5] O. Roesler, “EEG Eye State Data Set,” Baden-Wuerttemberg Cooperative State University (DHBW), 2013. [Online]. Available: https://archive.ics.uci.edu/ml/datasets/EEG+Eye+State.
[6] B. Aubaidan, M. Mohd, M. Albared, and F. Author, “Comparative study of k-means and k-means++ clustering algorithms on crime domain,” J. Comput. Sci., vol. 10, no. 7, pp. 1197–1206, 2014, doi: 10.3844/jcssp.2014.1197.1206.
[7] C. M. Fikri, F. Eka, M. Agustin, and F. Mintarsih, “Pegawai Menggunakan Algoritma K-Means ++ Dan Cop-Kmeans Untuk Merencanakan Program Pemeliharaan Kesehatan Pegawai,” J. Pesudocode, vol. IV, pp. 9–17, 2017.
[8] L. P. Rizby, Marji, and L. Muflikhah, “Clustering Pasien Kanker Berdasarkan Struktur Protein Dalam Tubuh,” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 2, no. 10, pp. 3810–3816, 2018.
[9] W. A. Triyanto, “Algoritma K-Medoids Untuk Penentuan Strategi Pemasaran Produk,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 6, no. 1, p. 183, 2015, doi: 10.24176/simet.v6i1.254.
[10] M. Nascimento, F. Toledo, and A. Carvalho, “A hybrid heuristic for the k-medoids clustering problem,” GECCO’12 - Proc. 14th Int. Conf. Genet. Evol. Comput., pp. 417–424, 2012, doi: 10.1145/2330163.2330223.
[11] R. Hadi, I. K. Gede Darma Putra, and I. N. Satya Kumara, “Penentuan Kompetensi Mahasiswa dengan Algoritma Genetik dan Metode Fuzzy C-Means,” Maj. Ilm. Teknol. Elektro, vol. 15, no. 2, pp. 101–106, 2017, doi: 10.24843/mite.1502.15.
[12] P. E. Mas’udia and R. Wardoyo, “Optimasi Cluster Pada Fuzzy C-Means Menggunakan Algoritma Genetika Untuk Menentukan Nilai Akhir,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 6, no. 1, pp. 101–110, 2013, doi: 10.22146/ijccs.2145.
[13] M. E. Al Rivan, S. Steven, and W. Tanzil, “Optimasi Fuzzy C-Means dan K-Means Menggunakan Algoritma Genetika untuk Pengklasteran Dataset Diabetic Retinopathy,” J. Teknol. Inf. dan Ilmu Komput., 2020, doi: 10.25126/jtiik.2020711872.
[14] V. A. P. S, “Perbandingan Algoritma K-Means dan Algoritma K-Medoids dalam Pengelompokan Komoditas Peternakan di Provinsi Jawa Tengah Tahun 2015,” Tugas Akhir Jur. Stat. Fak. Mat. dan Ilmu Pengetah. Alam Univ. Islam Inndonesia Yogyakarta, vol. 53, no. 9, pp. 1689–1699, 2018, doi: 10.1017/CBO9781107415324.004.
[15] E. C. Prakoso, U. N. Wisesty, and . J., “Klasifikasi Keadaan Mata Berdasarkan sinyal EEG menggunakan Extreme Learning Machines,” Indones. J. Comput., vol. 1, no. 2, p. 97, 2016, doi: 10.21108/indojc.2016.1.2.105.
[2] A. Al Malki, M. M. Rizk, M. A. El-Shorbagy, and A. A. Mousa, “Hybrid Genetic Algorithm with K-Means for Clustering Problems,” Open J. Optim., vol. 05, no. 02, pp. 71–83, 2016, doi: 10.4236/ojop.2016.52009.
[3] G. Yamini and B. R. Devi, “A New Hybrid Clustering Technique Based On Mini-batch K-means And K-means++ For Analysing Big Data.,” Int. J. Recent Res. Asp., no. April, pp. 203–208, 2018.
[4] W. Sheng and X. Liu, “A Genetic K-medoids Clustering Algorithm,” J. Heuristics, vol. 12, no. 6, pp. 447–466, 2006.
[5] O. Roesler, “EEG Eye State Data Set,” Baden-Wuerttemberg Cooperative State University (DHBW), 2013. [Online]. Available: https://archive.ics.uci.edu/ml/datasets/EEG+Eye+State.
[6] B. Aubaidan, M. Mohd, M. Albared, and F. Author, “Comparative study of k-means and k-means++ clustering algorithms on crime domain,” J. Comput. Sci., vol. 10, no. 7, pp. 1197–1206, 2014, doi: 10.3844/jcssp.2014.1197.1206.
[7] C. M. Fikri, F. Eka, M. Agustin, and F. Mintarsih, “Pegawai Menggunakan Algoritma K-Means ++ Dan Cop-Kmeans Untuk Merencanakan Program Pemeliharaan Kesehatan Pegawai,” J. Pesudocode, vol. IV, pp. 9–17, 2017.
[8] L. P. Rizby, Marji, and L. Muflikhah, “Clustering Pasien Kanker Berdasarkan Struktur Protein Dalam Tubuh,” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 2, no. 10, pp. 3810–3816, 2018.
[9] W. A. Triyanto, “Algoritma K-Medoids Untuk Penentuan Strategi Pemasaran Produk,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 6, no. 1, p. 183, 2015, doi: 10.24176/simet.v6i1.254.
[10] M. Nascimento, F. Toledo, and A. Carvalho, “A hybrid heuristic for the k-medoids clustering problem,” GECCO’12 - Proc. 14th Int. Conf. Genet. Evol. Comput., pp. 417–424, 2012, doi: 10.1145/2330163.2330223.
[11] R. Hadi, I. K. Gede Darma Putra, and I. N. Satya Kumara, “Penentuan Kompetensi Mahasiswa dengan Algoritma Genetik dan Metode Fuzzy C-Means,” Maj. Ilm. Teknol. Elektro, vol. 15, no. 2, pp. 101–106, 2017, doi: 10.24843/mite.1502.15.
[12] P. E. Mas’udia and R. Wardoyo, “Optimasi Cluster Pada Fuzzy C-Means Menggunakan Algoritma Genetika Untuk Menentukan Nilai Akhir,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 6, no. 1, pp. 101–110, 2013, doi: 10.22146/ijccs.2145.
[13] M. E. Al Rivan, S. Steven, and W. Tanzil, “Optimasi Fuzzy C-Means dan K-Means Menggunakan Algoritma Genetika untuk Pengklasteran Dataset Diabetic Retinopathy,” J. Teknol. Inf. dan Ilmu Komput., 2020, doi: 10.25126/jtiik.2020711872.
[14] V. A. P. S, “Perbandingan Algoritma K-Means dan Algoritma K-Medoids dalam Pengelompokan Komoditas Peternakan di Provinsi Jawa Tengah Tahun 2015,” Tugas Akhir Jur. Stat. Fak. Mat. dan Ilmu Pengetah. Alam Univ. Islam Inndonesia Yogyakarta, vol. 53, no. 9, pp. 1689–1699, 2018, doi: 10.1017/CBO9781107415324.004.
[15] E. C. Prakoso, U. N. Wisesty, and . J., “Klasifikasi Keadaan Mata Berdasarkan sinyal EEG menggunakan Extreme Learning Machines,” Indones. J. Comput., vol. 1, no. 2, p. 97, 2016, doi: 10.21108/indojc.2016.1.2.105.