Clustering Provinsi Luar Pulau Jawa Berdasarkan Data Pembangkitan Energi Listrik Menggunakan Metode Fuzzy C-Means
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Listiyani, Suci Ratna
Susanti, Meilia Nur Indah
Karmila, Sely
Susanti, Meilia Nur Indah
Karmila, Sely
Subject
Teknik Informatika
Datestamp
2022-09-26 06:32:52
Abstract :
ABSTRACT Electricity consumption in Indonesia is increasingly marked by the rapid growth of technology and the national economy. Electricity needs are important for the community for that information related to The research aims to implement the Fuzzy C-Means method to group provincial regions based on generating data with attributes obtained from PLN statistics books for 2017-2018. The Fuzzy C-Means method is an easy algorithm and is often used in clustering techniques or data grouping. Some studies have concluded that the Fuzzy C-Means algorithm can be used to group data based on specific attributes. In this study, Fuzzy C-Means algorithm will be used to group data from 29 provinces outside java based on electrical energy generation data from the electrical statistic book. The study also tested the accuracy of each cluster using the Davies Bouildin Index method. The results of this study suggest that the 4th cluster in each of the data in 2017 and 2018 is the most optimal cluster with the least DBI value acquisition of all clusters
ABSTRACT Electricity consumption in Indonesia is increasingly marked by the rapid growth of technology and the national economy. Electricity needs are important for the community for that information related to The research aims to implement the Fuzzy C-Means method to group provincial regions based on generating data with attributes obtained from PLN statistics books for 2017-2018. The Fuzzy C-Means method is an easy algorithm and is often used in clustering techniques or data grouping. Some studies have concluded that the Fuzzy C-Means algorithm can be used to group data based on specific attributes. In this study, Fuzzy C-Means algorithm will be used to group data from 29 provinces outside java based on electrical energy generation data from the electrical statistic book. The study also tested the accuracy of each cluster using the Davies Bouildin Index method. The results of this study suggest that the 4th cluster in each of the data in 2017 and 2018 is the most optimal cluster with the least DBI value acquisition of all clusters
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