PENGELOMPOKAN SEBARAN TRANSFORMATOR DISTRIBUSI BERDASARKAN KAPASITAS DAYA MENGGUNAKAN METODE NAÏVE BAYES Studi Kasus: PT. PLN RAYON KOTA SAMARINDA
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Rheo Malani(1*)
Politeknik Negeri Samarinda
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Bedi Suprapty(2)
Politeknik Negeri Samarinda
(*) Corresponding Author
Abstract
ABSTRACT
Human needs for energy are mostly obtained from electrical energy, both for daily needs and for industrial needs. PT. PLN (Persero) is one of the state electricity companies that serves the community's need for electricity. Transformer or better known as "transformer" or "transformer" is actually an electrical device that converts AC power at one voltage level to one voltage level based on the principle of electromagnetic induction without changing its frequency. Because of the lack of distribution of transformers around the Samarinda area, it can result in electricity demand services to the community. Therefore we need a method that can facilitate the distribution of PT. PLN Rayon Kota Samarinda, one of the methods is by applying Naïve Bayes. The purpose of this study is to facilitate the distribution in each region and the type of transformer used. The results of calculations using the Naïve Bayes method, obtained the probability of grouping the training data is P (160) = 0.006441224, P (100) = 0.016304348, P (80) = 0.001610306, P (50) = 0.001610306, P (40) = 0.000402576, P P (20) = 0,000679348. From the calculation results, it appears that the probability value P (100) is more dominant, then 100 is recommended for real consumption which is used as training data. The Naïve Bayes method produces an accuracy rate of 92%.
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References
A. Gumay, “Transformator,” dari http://eprints.polsri.ac.id, pp. 1-31, 2015.
A. Saleh, “Implementasi Metode Klasifikasi Naïve Bayes Dalam Memprediksi Besarnya Penggunaan Listrik Rumah Tangga,” Citec Journal, vol. 2, no. 3, pp. 207-217, 2015.
A. Saleh, “Implementasi Metode Klasifikasi Naïve Bayes Dalam Memprediksi Besarnya Penggunaan Listrik Rumah Tangga ISSN : 2354-5771,” Citec Journal, vol. 2, no. 3, pp. 207-217, 2015.
E. Irwansyah, and M. Faisal, Advanced Clustering: Teori dan Aplikasi, p.^pp. 4-5: DeePublish, 2015.
Bustami, “PENERAPAN ALGORITMA NAIVE BAYES UNTUK MENGKLASIFIKASI DATA NASABAH ASURANSI,” JUrnal Informatika, vol. 8, no. 1, pp. 884-898, 2014.
M. Ridwan, H. Suyono, and M. Sarosa, “Penerapan Data Mining Untuk Evaluasi Kinerja Akademik Mahasiswa Menggunakan Algoritma Naive Bayes Classifier,” JURNAL EECCIS, vol. 7, no. 1, pp. 59-64, 2013.
R. G. Rafsanjani, N. Hidayat, and R. K. Dewi, “Diagnosis Penyakit Hati Menggunakan Metode Naive Bayes Dan Certainty Factor,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 11, pp. 4478-4482, 2018.
R. Malani, and B. Suprapty,"Implementasi Sistem Seleksi Kinerja Pegawai Negeri Khususnya Tenaga Pengajar Terbaik Menggunakan Metode K-Means Clustering Pada Politeknik Negeri Samarinda," Seminar Nasional Teknologi Terapan, Vol.3, pp.1491, 2016
P.R. Nastiti, and A.B.W. Putra, "Perbandingan Algoritma K-Means dan Fuzzy C-Means Clustering Untuk Kualifikasi Data Kinerja Dosen di Jurusan Teknologi Informasi POLNES," Prosiding Seminar SEBATIK, Vol.1, No.1, pp.71-76, 2017
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