Data Mining Prediksi Besarnya Penggunaan Listrik Rumah Tangga di Kota Batam Dengan Menggunakan Algoritma C4.5

  • Yulia Yulia
  • Nurul Azwanti

Abstract

Human activity in the use of electricity has increased over time - time. This is due to electrical energy has become an important part for the development of human civilization in various fields including economic, technological, social and human culture. Strategy forecasting the need for electrical energy is needed. People's need for electric energy continues to grow every year. In addition to population growth, the economic growth of a region is believed to be one of the factors affecting the increasing consumption of electrical energy in the area. As the city of Batam  in Batam Center area which is an industrial city and the population is fairly solid. Batam Center area includes the central area of ​​Batam city because the area is close to Hang Nadim Airport Batam and Batam International Port Center. Therefore every household should understand the effective use of electricity so that the electricity needs do not become greater than the electricity supply. Data mining techniques with C4.5 algorithm can predict the use of household electricity to more easily regulate the use of household electricity. The sample data is taken as many as 30 correspondent data that use electricity meter in Batam Center area. The number of electronic goods, the number of users, the length of time at home and the area of ​​the house building will be variable in analyzing the data. There are variables Wide of Home Build and Number of Family Members become decision forming tree variable. The calculation results have been tested using Weka 3.7.4 with the same rule result.

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Published
2018-10-17
How to Cite
YULIA, Yulia; AZWANTI, Nurul. Data Mining Prediksi Besarnya Penggunaan Listrik Rumah Tangga di Kota Batam Dengan Menggunakan Algoritma C4.5. Prosiding Seminar Nasional Ilmu Sosial dan Teknologi (SNISTEK), [S.l.], n. 1, p. 175-180, oct. 2018. Available at: <http://ejournal.upbatam.ac.id/index.php/prosiding/article/view/766>. Date accessed: 21 june 2019.
Section
Articles