IMPLEMENTASI DATAMINING PADA KASUS TENAGA LISTRIK YANG DIBANGKITKAN BERDASARKAN PROVINSI
DOI:
https://doi.org/10.30865/komik.v3i1.1683Abstract
Indonesia is one of the most vital electric energy users. The development of the world of technology and information in its use does not escape from access to electricity. This study discusses the Implementation of Datamining in the Case of Electric Power Generated by Province. The increasing need for electricity usage from time to time has never escaped the attention and auspices of the government. The data source in this study was accessed from the official website of the Indonesian government, namely the Central Statistics Agency (http://www.bps.go.id). The data used in this study are data from 2011-2017 which consists of 33 provinces in Indonesia. In the analysis of this study using 3 (three) cluster levels, namely the first high level cluster (C1), the second moderate level cluster (C2) and the third low level cluster (C3). So that the final results of the analysis of the case study of Electric Power Generating by Province obtained new data and information, namely the high cluster province of 2 provinces namely East Java and Banten, the medium cluster province of 4 provinces namely North Sumatra, South Sumatra, West Java and Central Java while low cluster provinces as much as 27 in other provinces. The results of the analysis of this study can be used as input for the government and the State Electricity Company (PLN), in order to make the province of the highest cluster category a top priority in increasing the growth of power plants as well as being more interactive in the utilization of electricity effectively and efficiently.
Keywords: Data Mining, K-Means, Clustering, Energy, Electric Power, ProvinceReferences
Aline Embun Pramadhani and T. Setiadi, “Penerapan Data Mining Untuk Klasifikasi Prediksi Penyakit ISPA (Infeksi Saluran Pernapasan Akut) Dengan Algoritma Decision Tree (ID3),†J. Sarj. Tek. Inform., vol. 2, no. 1, pp. 831–839, 2014.
I. Parlina, A. P. Windarto, A. Wanto, and M. R. Lubis, “Memanfaatkan Algoritma K-Means Dalam Menentukan Pegawai Yang Layak Mengikuti Asessment Center Untuk Clustering Program SDP,†CESS (Journal Comput. Eng. Syst. Sci., vol. 3, no. 1, pp. 87–93, 2018.
A. P. Windarto, “Implementation of Data Mining on Rice Imports by Major Country of Origin Implementation of Data Mining on Rice Imports by Major Country of Origin Using Algorithm Using K-Means Clustering Method,†Int. J. Artif. Intelegence Res., vol. 1, no. 2, pp. 26–33, 2017.
H. Santosa, “Aplikasi Penentuan Tarif Listrik Menggunakan Metode Fuzzy Sugeno,†J. Sist. Inf. Bisnis, vol. 01, no. 14, pp. 28–39, 2014.
A. Widarma and H. Kumala, “Sistem Pendukung Keputusan Dalam Menentukan Pengguna Listrik Subsidi Dan Nonsubsidi Menggunakan Metode Fuzzy Mamdani ( Studi Kasus : PT . PLN Tanjung Balai ),†J. Teknol. Inf., vol. 2, no. 2, pp. 165–171, 2018.
D. Despa et al., “Prediksi kebutuhan listrik tiga fase dengan jaringan syaraf tiruan berdasarkan data erte system universitas lampung,†SEBATIK, pp. 203–210, 2019.
I. Sudono et al., “Pengelompokan Produksi Padi Nasional Dengan Pendekatan Data Mining Konsep K-Means Clustering National Rice Production In Mining Data Approach Concept Of K-Means,†J. Irig., vol. 8, no. 2, pp. 72–89, 2013.
A. P. Windarto, P. Studi, S. Informasi, and D. Mining, “Penerapan Data Mining Pada Ekspor Buah-Buahan Menurut Negara Tujuan Menggunakan K-Means Clustering,†NTechno.COM, vol. 16, no. 4, pp. 348–357, 2017.
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.
