DOI: https://doi.org/10.14710/j.gauss.v11i2.35470

Kernel K-Means Clustering untuk Pengelompokan Sungai di Kota Semarang Berdasarkan Faktor Pencemaran Air

*Anestasya Nur Azizah  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Tatik Widiharih  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Arief Rachman Hakim  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Open Access Copyright 2022 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
K-Means Clustering is one of the types of non-hierarchical cluster analysis which is frequently used, but has a weakness in processing data with non-linearly separable (do not have clear boundaries) characteristic and overlapping cluster, that is when visually the results of a cluster are between other clusters. The Gaussian Kernel Function in Kernel K-Means Clustering can be used to solve data with non-linearly separable characteristic and overlapping cluster. The difference between Kernel K-Means Clustering and K-Means lies on the input data that have to be plotted in a new dimension using kernel function. The real data used are the data of 47 rivers and 18 indicators of river water pollution from Dinas Lingkungan Hidup (DLH) of Semarang City in the first semester of 2019. The cluster results evaluation is used the Calinski-Harabasz, Silhouette, and Xie-Beni indexes. The goals of this study are to know the step concepts and analysis results of Kernel K-Means Clustering for the grouping of rivers in Semarang City based on water pollution factors. Based on the results of the study, the cluster results evaluation show that the best number of clusters K=4
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Keywords: Gaussian Kernel; Kernel K-Means Clustering; Cluster Results Evaluation

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  1. Aprianto, K. 2018. Optimasi Kernel K-Means dalam Pengelompokan Kabupaten/Kota Berdasarkan Indeks Pembangunan Manusia di Indonesia. Journal of Mathematics and Its Applications Vol. 15, No. 1, Hal: 1-15
  2. Bilson, S. 2005. Analisis Multivariat Pemasaran. Jakarta: Gramedia Pustaka Utama
  3. Cristianini, N., dan Taylor, J.S. 2000. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. United Kingdom: Cambridge University Press
  4. Girolami, M. 2002. Mercer Kernel-Based Clustering in Feature Space. Journal Transaction on Neural Networks Vol.13, No 3, Hal: 780-784
  5. Gujarati, D. N. 2004. Basic Econometrics (4th ed.). New York: The McGraw-Hill
  6. Hair, J. F. Jr., Black, W. C., Barry, J. B., dan Anderson, R. E. 2010. Multivariate Data Analysis Seventh Edition. New Jersey: Pearson Education
  7. Han, J., dan Kamber, M. 2006. Data Mining: Concepts and Techniques Second Edition. San Fransisco: Morgan Kauffmann
  8. Indraswari, R., Arifin, A. Z., dan Herumurti, D. 2017. RBF Kernel Optimization Method With Particle Swarm Optimization On SVM Using The Analysis Of Input Data’s Movement. Journal of Computer Science and Information Vol. 10, No. 1, Hal: 36-42
  9. Liu, Y., Li, Z., Xiong, H., Gao, X., dan Wu, J. 2010. Understanding of Internal Clustering Validation Measures. Proceeding of IEEE International Conference on Data Mining. IEEE New York: 13-17 Desember 2010
  10. Maulik, U., dan Bandyopadhyay, S. 2002. Performance Evaluation of Some Clustering Algorithms and Validity Indices. Journal: IEEE Transactions On Pattern Analysis And Machine Intelligence Vol. 24, No. 12, Hal: 1650-1654
  11. Maysaroh, S. 2015. Analisis Kelompok Dengan Metode Kernel K-Means (Studi Kasus Pengelompokan Kabupaten/Kota di Indonesia Berdasarkan Penduduk Dengan Faktor-Faktor Risiko Penyebab Penyakit Hipertensi). Tesis. Program Studi Statistika Fakultas Matematika dan Ilmu Pengetahuan Alam Institut Teknologi Sepuluh Nopember Surabaya
  12. Murfi, H. 2009. Metode Kernel. Bahan Kuliah: Machine Learning. Program Studi Matematika Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Indonesia Depok
  13. Widiyanto, M. T. A. C. 2019. Perbandingan Validitas Fuzzy Clustering pada Fuzzy C-Means Dan Particle Swarms Optimazation (PSO) pada Pengelompokan Kelas. Jurnal Informatika Sunan Kalijaga Vol. 4, No. 1, Hal: 22-37

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