APPLICATION OF K-NEAREST NEIGHBORS MODEL IN ELECTRICAL POWER NEEDS CLASSIFICATION FOR EACH REGION IN LHOKSEUMAWE CITY
Abstract
Classification for electric power requirements for each region is very necessary in order to describe the power conditions needed This is very important for new customers to want to know the power provided, otherwise old customers can also see and reduce power or add power according to needs. The variable used is the area of ??the house, the amount of electric power that will be used and has been used, the combined income of parents (dirty) / month, the amount of power of the lights at home, then continued with the classification of the estimated electrical power provided. Furthermore, the classification used is the determination of the class of Tariff / Power R-1/450 VA subsidies, R-1/900 VA subsidies, R-1/900 VA-RTM (capable Household) non-subsidized, R-1/1300 VA non-subsidized, and Tariff / Power class R-1/2200 VA non-subsidized. Furthermore, for testing using training data samples as many as 20 sample data from each customer that will be seen with the closest neighbors. For power samples consist of testing variables and classification types. K-Nearest Neighbors (KNN) test for house area is 3, power 3, income 2, total power, 3 and energy consumption used is 4. Results from this research is the application of technology in the KNN in the determination of the power requirements for each region at Lhokseumawe City.
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References
[2] Chang, P. C., Fan, C.Y. dan Hsieh, J. C., 2009. A Weighted Evolving Fuzzy Neural Network for Electricity Demand Forecasting. IEEE, pp.330-35.
[3] Antonov, Rahman Arief, 2015 Provinsi Sumatera Barat Hingga Tahun 2024 Dengan Metode Analisis Regresi Linear Berganda, Jurnal Teknik Elektro ITP, Volume 4, No. 2; Juli 2015 ISSN 2252-3472
[4] D. Kiron, R. Shockley, N. Kruschwitz, G. Finch, andM. Haydock, Analytics: The Widening Divide, MIT Sloan Management Review, 53(2), 1-22, 2012.
[5] Goujon G, Chaoqun, Jianhong W. Data Clusterin :Theory, Algorithms, and Applications. Virginia: ASA;2007.
[6] Han, J. dan Kamber, M. 2006. Data Mining Concepts and Techniques Second Edition. Morgan Kaufmann Publishers.
[7] Kusrini, 2009, Algoritma Data Mining, Andi Offset, Yogyakarta, 2009.
[8] Larose, D. T. 2005. Discovering Knowledge in Data: An Introduction to Data Mining. Wiley, Chichester.
[9] Tri Halomoan Simanjuntak, Wayan Firdaus Mahmudy dan Sutrisno. 2014. Implementasi Modified K-Nearest Neighbor dengan Otomatisasi Nilai K Pada Pengklasifikasian Penyakit Tanaman Kedelai. online : http://j- ptiik.ub.ac.id/index.php/jptiik/article/download/15/21/
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