A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Prediksi Perkembangan Jumlah Pelanggan Listrik Menurut Pelanggan Area Menggunakan Algoritma Backpropagation
Electricity is one of the vital needs of humanity. Without electricity, it is certain that the wheels of the economy will not be able to run properly. So that electricity customers are increasingly increasing, as they increase the needs and population of the community. Therefore this study aims to determine the development of the number of electricity customers using the backpropagation algorithm. The research data used was electricity customer data by area (customer) in North Sumatra in 2013-2017, obtained from the Central Statistics Agency of North Sumatra. This study uses 5 architectural models, namely 4-2-1, 4-3-1, 4-4-1, 4-5-1, and 4-6-1. Of the five architectural models used, one of the best architectural models is obtained 4-4-1 with an accuracy rate of 88%, epoch 716 iterations in a short amount of time, 15 seconds, with MSE Training 0,00099763 and MSE testing 0.00109935. Based on the best architectural model, this will be used to predict the Development of Electricity Customers by Area Customers in North Sumatra from 2018 to 2020
Prediksi Perkembangan Jumlah Pelanggan Listrik Menurut Pelanggan Area Menggunakan Algoritma Backpropagation
Electricity is one of the vital needs of humanity. Without electricity, it is certain that the wheels of the economy will not be able to run properly. So that electricity customers are increasingly increasing, as they increase the needs and population of the community. Therefore this study aims to determine the development of the number of electricity customers using the backpropagation algorithm. The research data used was electricity customer data by area (customer) in North Sumatra in 2013-2017, obtained from the Central Statistics Agency of North Sumatra. This study uses 5 architectural models, namely 4-2-1, 4-3-1, 4-4-1, 4-5-1, and 4-6-1. Of the five architectural models used, one of the best architectural models is obtained 4-4-1 with an accuracy rate of 88%, epoch 716 iterations in a short amount of time, 15 seconds, with MSE Training 0,00099763 and MSE testing 0.00109935. Based on the best architectural model, this will be used to predict the Development of Electricity Customers by Area Customers in North Sumatra from 2018 to 2020
Prediksi Perkembangan Jumlah Pelanggan Listrik Menurut Pelanggan Area Menggunakan Algoritma Backpropagation
Saragih, Irfan Christian (author) / Hartama, Dedy (author) / Wanto, Anjar (author)
2020-06-30
doi:10.47065/bits.v2i1.341
Building of Informatics, Technology and Science (BITS); Vol 2 No 1 (2020): June 2020; 48-53 ; 2685-3310 ; 2684-8910
Article (Journal)
Electronic Resource
English
DDC:
720
Prediksi Indeks Pembangunan Manusia di Kabupaten Wonosobo Menggunakan Algoritma Backpropagation
| DOAJ | 2023
Segmentasi Pelanggan B2B dengan Model LRFM Menggunakan Algoritma Fuzzy C-Means pada Rotte Bakery
| DOAJ | 2023
PENGARUH JUMLAH PELANGGAN PLN, HOTEL, INDUSTRI TERHADAP PENERIMAAN PAJAK PENERANGAN JALAN KOTA
| DOAJ | 2017