@thesis{thesis,
		author={Haris  Abdul and Indrianto  Indrianto and SEMI  MIFTAUL JATZIA},
		title ={MODEL KOMPUTASI CERDAS UNTUK KLASIFIKASI TINGGI RENDAH DAYA LISTRIK PADA GARDU MENGGUNAKAN METODE LEARNING VECTOR QUANTIZATION (LVQ)},
		year={2023},
		url={http://156.67.221.169/5677/},
		abstract={The increasing demand and number of consumers is directly proportional to the 
increasing demand for electrical loads from time to time. If demand is greater than 
supply or vice versa, there will be a waste of electricity and system failure. the 
classification of the use of short-term electrical power loads is needed for control and 
consideration in scheduling maintenance of the electric power system in an effort to 
reduce problems that arise due to imbalances in demand and supply. distributed and 
stored for short-, medium- and long-term needs. The Learning Vector Quantization 
method is an artificial neural network that is used for grouping inputs to be given and 
learning at the competitive layer that is automatically supervised and is one of the
supervised learning methods that can be implemented in the problem of classifying the 
use of power loads at electrical substations. This system will manage data in the form of 
electricity consumption in June 2020 as training data and test data using data in the 
following month. The parameters used to get the best accuracy results are learning rate 
0.01, hidden 10 and epoch 100 with a total of 30 days of data. Based on the test results, 
the accuracy in June was 96,6667%. By using the June 2020 substation power load 
data, it can be applied as a reference for classification in the Learning Vector 
Quantization method for power load classification at electrical substations.}
		}