PENINGKATAN KINERJA ALGORITMA K MEANS DENGAN MENGGUNAKAN PARTICLE SWARM OPTIMIZATION DALAM PENGELOMPOKAN DATA PENYEDIAAN AKSES SANITASI DAN AIR BERSIH
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Nov 3, 2020
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Abstract
Water is one of the things that plays a very important role in human survival, because the Indonesian government has a community-based water supply and sanitation (PAMSIMAS) program, so that all the programs run well need a regional status grouping technique in this thesis. with the K-means algorithm.
K-means is a partition algorithm that aims to divide the data into the specified number of clusters, the results of the K means algorithm depend on the selection of the initial klater center but problems that often occur when selecting the initial centroid are randomly drawn from the solution. from the grouping is not quite right. To overcome this problem the author wants to use the PSO algorithm in the initial centroid selector for the K-means algorithm, in this study also compared the selection of the first 3 centroids according to random, second according to government standards the value of high, medium and low drinking water quality then the third method proposed by the PSO algorithm was then tested with Davies Bouldin Index.
From the test results, the K-means method with the selection of random initial centroid with a value of 0.208856082, the K-means method with the selection of centroids in accordance with government standards about SAM conditions of 0.280077 and the best selection method is K-means PSO 0, 08383. So testing the PAMSIMAS data using K-means PSO found that the method was more optimal.
K-means is a partition algorithm that aims to divide the data into the specified number of clusters, the results of the K means algorithm depend on the selection of the initial klater center but problems that often occur when selecting the initial centroid are randomly drawn from the solution. from the grouping is not quite right. To overcome this problem the author wants to use the PSO algorithm in the initial centroid selector for the K-means algorithm, in this study also compared the selection of the first 3 centroids according to random, second according to government standards the value of high, medium and low drinking water quality then the third method proposed by the PSO algorithm was then tested with Davies Bouldin Index.
From the test results, the K-means method with the selection of random initial centroid with a value of 0.208856082, the K-means method with the selection of centroids in accordance with government standards about SAM conditions of 0.280077 and the best selection method is K-means PSO 0, 08383. So testing the PAMSIMAS data using K-means PSO found that the method was more optimal.
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HENDRAWAN, A. Y. (2020). PENINGKATAN KINERJA ALGORITMA K MEANS DENGAN MENGGUNAKAN PARTICLE SWARM OPTIMIZATION DALAM PENGELOMPOKAN DATA PENYEDIAAN AKSES SANITASI DAN AIR BERSIH. Electro Luceat, 6(2), 213-227. https://doi.org/10.32531/jelekn.v6i2.245
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
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[25] M. Goyal and S. Kumar, “Improving the Initial Centroids of k-means Clustering Algorithm to Generalize its Applicability,” J. Inst. Eng. Ser. B, vol. 95, no. 4, pp. 345–350, 2014.
[26] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. 2012.
[27] M. E. Celebi, H. A. Kingravi, and P. A. Vela, “A comparative study of efficient initialization methods for the k-means clustering algorithm,” Expert Syst. Appl., vol. 40, no. 1, pp. 200–210, 2013.
[28] J. Wu, Advanced in K-means Clustering. 2012.
[29] C. Grosan, A. Abraham, and M. Chis, “Swarm Intelligence in Data Mining,” vol. 34, no. 2006, pp. 1–20, 2006.
[30] D. L. Davies and D. W. Bouldin, “A Cluster Separation Measure,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-1, no. 2, pp. 224–227, 1979.
[31] F. Cao, J. Liang, and G. Jiang, “An initialization method for the K-Means algorithm using neighborhood model,” Comput. Math. with Appl., vol. 58, no. 3, pp. 474–483, Aug. 2009.
[2] P. T. Penyusunan, R. Aksi, and T. Pembangunan, “DRAFT,” 2017.
[3] Unicef, “Air Bersih, Sanitasi & Kebersihan,” Ringkasan Kaji., pp. 1–6, 2012.
[4] D. Jenderal and C. Karya, pedoman umum pengelolaan PAMSIMAS. .
[5] W. li Xiang, N. Zhu, S. feng Ma, X. lei Meng, and M. qing An, “A dynamic shuffled differential evolution algorithm for data clustering,” Neurocomputing, vol. 158, pp. 144–154, 2015.
[6] Z. Huang, “Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values,” Data Min. Knowl. Discov., vol. 2, no. 3, pp. 283–304, 1998.
[7] D. W. Van der Merwe and A. P. Engelbrecht, “Data clustering using particle swarm optimization,” Evol. Comput. 2003. CEC’03. 2003 Congr., vol. 1, pp. 215–220, 2003.
[8] J. Kennedy and R. Eberhart, “Particle swarm optimization,” Neural Networks, 1995. Proceedings., IEEE Int. Conf., vol. 4, pp. 1942–1948 vol.4, 1995.
[9] S. Petrovic, “A Comparison Between the Silhouette Index and the Davies-Bouldin Index in Labelling IDS Clusters,” Tthe 11th Nord. Work. Secur. IT-systems, Nord. 2006, p. 53–64., 2006.
[10] R. O. Ph, Advances in K-means Clustering A Data Mining Thinking Doctoral. .
[11] F. Nasari and C. J. M. Sianturi, “Penerapan Algoritma K-Means Clustering Untuk Pengelompokkan Penyebaran Diare Di Kabupaten Langkat,” Cogito Smart J., vol. 2, no. 2, pp. 108–119, 2016.
[12] N. H. Kristanto, A. C. L. A, and H. B. S, “Implemantasi K-Means Clustering untuk Pengelompokan Analisis Rasio Profitabilitas dalam Working Capital,” Juisi, vol. 02, no. 01, pp. 9–15, 2016.
[13] I. Pendahuluan, K. Ilmiah, and D. Mining, “Metode Manhattan , Euclidean Dan Chebyshev Pada Algoritma K-Means Untuk Pengelompokan Status Desa,” 2016.
[14] P. D. W. Mega, “Clustering Menggunakan Metode K-Means untuk Menentukan Status Gizi Balita,” J. Inform., vol. 15, no. 2, pp. 160--174, 2015.
[15] B. Darma, D. Setiawan, and R. S. Perdana, “Algoritme Genetika Untuk Optimasi K-Means Clustering Dalam Pengelompokan Data Tsunami,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 10, pp. 3865–3872, 2018.
[16] N. Yusup, A. M. Zain, and S. Z. M. Hashim, “Overview of PSO for optimizing process parameters of machining,” Procedia Eng., vol. 29, pp. 914–923, 2012.
[17] Y. Eka, A. Stmik, N. M. Jakarta, and K. Kunci, “Penerapan Metode Particle Swarm Optimization Pada Optimasi Prediksi Pemasaran Langsung,” Desember, vol. 5, no. 1, pp. 1–11, 2018.
[18] E. R. Zerda, “Analisis dan Penerapan Algoritma Particle Swarm Optimization ( PSO ) pada Optimasi Penjadwalan Sumber Daya Proyek,” Tugas Akhir (Skripsi), p. Cover, i–xi, 1-42, 2009.
[19] A. Yasid, “Implementasi automatic clustering menggunakan particle swarm optimization dan genetic algorithm pada data kemahasiswaan,” pp. 9–10, 2015.
[20] A. Saidul and J. L. Buliali, “Implementasi Particle Swarm Optimization pada K-Means untuk Clustering Data Automatic Dependent Surveillance-Broadcast,” Eksplora Inform., vol. 8, no. 1, p. 30, 2018.
[21] M. Teja, A. Cipta, and F. C-, “C-Means Dalam Pengelompokan Kelas,” vol. 11, no. 1, pp. 72–91, 2018.
[22] M. Al Hasan, V. Chaoji, S. Salem, and M. J. Zaki, “Robust partitional clustering by outlier and density insensitive seeding,” Pattern Recognit. Lett., vol. 30, no. 11, pp. 994–1002, 2009.
[23] M. M. Breunig et al., “LOF: identifying density-based local outliers,” Proc. 2000 ACM SIGMOD Int. Conf. Manag. data - SIGMOD ’00, vol. 29, no. 2, pp. 93–104, 2000.
[24] S. S. Khan and A. Ahmad, “Cluster center initialization algorithm for K-modes clustering,” Expert Syst. Appl., vol. 40, no. 18, pp. 7444–7456, 2013.
[25] M. Goyal and S. Kumar, “Improving the Initial Centroids of k-means Clustering Algorithm to Generalize its Applicability,” J. Inst. Eng. Ser. B, vol. 95, no. 4, pp. 345–350, 2014.
[26] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. 2012.
[27] M. E. Celebi, H. A. Kingravi, and P. A. Vela, “A comparative study of efficient initialization methods for the k-means clustering algorithm,” Expert Syst. Appl., vol. 40, no. 1, pp. 200–210, 2013.
[28] J. Wu, Advanced in K-means Clustering. 2012.
[29] C. Grosan, A. Abraham, and M. Chis, “Swarm Intelligence in Data Mining,” vol. 34, no. 2006, pp. 1–20, 2006.
[30] D. L. Davies and D. W. Bouldin, “A Cluster Separation Measure,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-1, no. 2, pp. 224–227, 1979.
[31] F. Cao, J. Liang, and G. Jiang, “An initialization method for the K-Means algorithm using neighborhood model,” Comput. Math. with Appl., vol. 58, no. 3, pp. 474–483, Aug. 2009.