CLUSTERISASI PEMAKAIAN ENERGI LISTRIK DI PROVINSI LAMPUNG BERBASIS WEB (STUDI KASUS : PT. PLN (PERSERO) UP2D LAMPUNG)
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Perkasa, Pimar Aprilo Jujur
Asri, Yessy
Haris, Abdul
Asri, Yessy
Haris, Abdul
Subject
Teknik Informatika
Datestamp
2022-09-26 06:41:21
Abstract :
The use of electrical energy is necessary for every member of the community. In 2019, the use of electric power will work normally, whether it is for households, industries and businesses. But in 2020, the use of electric energy will see a drastic increase, especially for households. Usage has increased due to the corona virus (covid-19) pandemic forcing peopole to work from home to reduce the spread of the corona virus. When the, corona virus ,(covid-19) pandemic occurred. PT.PLN (Persero) UP2D Lampung does not yet have an information system application for the cluster of electric energy consumption zones. Regarding this issue, an application has been created that uses,the K-Means algorithm, to aggregate,the electricity consumption,of households, industries and neighborhoods, which produces red, yellow and green areas on the maps and there is a graphical display of electric powe consumption before the corona virus pandemic (covid-19). To test the accuacy of the clustering method using Davies Boulding (DBI) and Rapidminer tools. The results showed the level of accuracy of the K-Means algorithm model of -0,400 for the classification by district/city
The use of electrical energy is necessary for every member of the community. In 2019, the use of electric power will work normally, whether it is for households, industries and businesses. But in 2020, the use of electric energy will see a drastic increase, especially for households. Usage has increased due to the corona virus (covid-19) pandemic forcing peopole to work from home to reduce the spread of the corona virus. When the, corona virus ,(covid-19) pandemic occurred. PT.PLN (Persero) UP2D Lampung does not yet have an information system application for the cluster of electric energy consumption zones. Regarding this issue, an application has been created that uses,the K-Means algorithm, to aggregate,the electricity consumption,of households, industries and neighborhoods, which produces red, yellow and green areas on the maps and there is a graphical display of electric powe consumption before the corona virus pandemic (covid-19). To test the accuacy of the clustering method using Davies Boulding (DBI) and Rapidminer tools. The results showed the level of accuracy of the K-Means algorithm model of -0,400 for the classification by district/city
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