KLASTERISASI DATA HISTORICAL SUSUT DAYA PADA PENGGUNAAN DAYA PELANGGAN AMR (AUTOMATIC METER READING) YANG TIDAK WAJAR MENGGUNAKAN METODE FUZZY C-MEANS PADA PT. PLN (PERSERO) DISTRIBUSI JAKARTA RAYA
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
RAMADANI, AKH. FARHAN
Asri, Yessy
Kuswardani, Dwina
Asri, Yessy
Kuswardani, Dwina
Subject
Teknik Informatika
Datestamp
2022-09-27 03:12:34
Abstract :
Power loss is a problem that is always faced by PT.PLN Distribution Jakarta Raya. Especially in non-technical losses which caused big losses for PT. PLN Distribution Jakarta Raya. AMR (Automatic Meter Reading) is used for reading or retrieving data from the measurement of electrical energy at each consumer locally or remotely. For that we need a method with high optimization in order to measure the losses that occur. This study uses the Fuzzy C-Means clustering method with the aim of comparing with Tupamahu's previous research (2018) which uses the K-Means method. Optimization of a cluster is determined by the minimum DBI (Davies-Bouldin index). The smaller the DBI value, then a cluster is said to be optimal for data clustering. The results prove that the Fuzzy C-Means method is more optimal than the K-Means method with the DBI value of the Fuzzy C-Means method of 0.54532171146831 in the cluster 2 set, while the DBI value of the K-Means method is 0.893 in the cluster set 4.
Power loss is a problem that is always faced by PT.PLN Distribution Jakarta Raya. Especially in non-technical losses which caused big losses for PT. PLN Distribution Jakarta Raya. AMR (Automatic Meter Reading) is used for reading or retrieving data from the measurement of electrical energy at each consumer locally or remotely. For that we need a method with high optimization in order to measure the losses that occur. This study uses the Fuzzy C-Means clustering method with the aim of comparing with Tupamahu's previous research (2018) which uses the K-Means method. Optimization of a cluster is determined by the minimum DBI (Davies-Bouldin index). The smaller the DBI value, then a cluster is said to be optimal for data clustering. The results prove that the Fuzzy C-Means method is more optimal than the K-Means method with the DBI value of the Fuzzy C-Means method of 0.54532171146831 in the cluster 2 set, while the DBI value of the K-Means method is 0.893 in the cluster set 4.
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