Analisis Data Sebaran Bandwidth Menggunakan Algoritma Dbscan Untuk Menentukan Tingkat Kebutuhan Bandwidth Di Kabupaten Purwakarta
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Abstract
Based on data recorded in 2018 there are 43 regional apparatus organizations in Purwakarta regency that have gained internet bandwidth. Each daetah device organization that has gained bandwidth has a different level of needs - but at this time the amount of bandwidth sharing and the level of needs cannot be grouped yet. The purpose of this study is to determine the level of bandwidth requirements in Purwakarta by analyzing data mining of existing data using the DBSCAN algorithm so that a cluster will be formed which is divided based on the level of need. In this study the analytical method used is SEMMA (Sample, Explore, Modify, Model, Assess) SEMMA stages include Data Selection, Pre-processing / cleaning, Transformation, Data Mining and Assess / Evaluation. The results of the analysis use the value of minpts = 5 and epsilon value = 3. Clusters formed are as many as 2 clusters, cluster 1 there are 15 regional device organizations with low bandwidth requirements and cluster 2 there are 21 regional device organizations with medium bandwidth requirements, and There are 7 regional organization devices with bandwidth requirements that are too high.
References
Sander, J., Ester, M., Kriegel, H.-P., & Xu, X. (1998). Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications. Int. Journal, Kluwer Academic Publishers.
Turban, E., Aronson, J. ., Liang, T. –., & McCarthy, T. –. (2006). Decision Support Systems, Pearson Education. Decision Support Systems, Pearson Education.
Larose, D. T. (2005). Discovering Knowledge in Data: An Introduction to Data Mining. New Jersey: John Wiley & Sons, Inc.
Azevedo, Santos, A., & F, M. (2008). KDD, SEMMA AND CRISP-DM: A PARALLEL OVERVIEW. IADIS.
Nagpal, P. ., & Mann, P. . (2011). Comparative study if density of based clustering algorithms. American Jurnal of Applications
Prasetyo, E. (2012). Data Mining Konsep dan Aplikasi Menggunakan Matlab. Yogyakarta: ANDI
Arsih, N., Hajarisman, N., & Darwis, S. (n.d.). Metode Pengclusteran Berbasis Densitas Menggunakan Algoritma DBSCAN. Bandung: Prodi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Islam Bandung.
Anindya Santika Devi, N.M., Gede Darma Putra, I.K. dan Sukarsa, I.M., 2015. Implementasi Metode Clustering DBSCAN pada Proses Pengambilan Keputusan. Lontar Komputer, 6(3), pp.185–191
lmanuel Ndaumanu, R., Kusrini dan Arief, M.R., 2014. Analisis Prediksi Tingkat Pengunduran Diri Mahasiswa dengan Metode K-Nearest Neighbor. Jatisi, 1(1).
Kusumo, D.S., Bijaksana, M.A. and Darmantoro, D., 2016. DATA MINING DENGAN ALGORITMA APRIORI PADA RDBMS ORACLE. TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika, 8(1), pp.1–5
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