Article Per Year (5 Year)
p-Index From 2021 - 2026
0
P-Index
This Author published in this journals
All Journal Pattimura Proceeding : Conference of Science and Technology
Y. A. Lesnussa
Department of Mathematics, Faculty of Mathematics and Natural Sciences Pattimura University
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Physics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
APPLICATION OF ARTIFICIAL NEURAL NETWORK BACKPROPAGATION TO PREDICT HOUSEHOLD CONSUMPTION OF ELECTRICITY IN AMBON S. H. Saija; Y. A. Lesnussa; F. Kondolembang
Pattimura Proceeding 2017: Proceedings of the 3rd International Seminar of Basic Science
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (510.174 KB) | DOI: 10.30598/PattimuraSci.2017.ICBS3.131-138

Abstract

Electricity is one of the energy most widely used in the universe. Electric power demand in Ambon city tends to increase due to the growing of population in Ambon. The necessary of electricity power in Ambon City by utilizing two systems are interconnected such as: PLTD Poka and PLTD Hative Kecil (Galala). In this research forecast the demand for household electricity consumption in 2016 based on validation data from 2011-2015 using Application of Neural Networks Backpropagation method. The validation data are using in JST-Backpropagation training, with the best network architecture that is 20 10 5 1 neurons and 0.8 learning rate, can produce the best pattern with the accuracy is 75% and the value of Mean Square Error is 0.298335.
APPLICATION OF ARTIFICIAL NEURAL NETWORK BACKPROPAGATION FOR PREDICTING THE AVAILIBILITY OF PREMIUM FUEL (CASE STUDY: PREDICTION OF PREMIUM FUEL AVAILIBILITY ON SPBU GALALA AMBON) Vicky Riyadi; E. R. Persulessy; Y. A. Lesnussa
Pattimura Proceeding 2017: Proceedings of the 3rd International Seminar of Basic Science
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (660.463 KB) | DOI: 10.30598/PattimuraSci.2017.ICBS3.139-148

Abstract

This study aims to predicting the availability of premium fuel using pattern recognition techniques named neural network with backpropagation method. Neural network is used to give solution for many problem, including taking a decision from the training data. Neural network can be applied to various specific fields of human life. In this study, Neural Network is used to predict the availability of premium fuel using backpropagation method. The data which is used in this study consist of 48 data, where 36 data (years 2012-2014) as training and 12 data (years 2015-2016) as testing. The result shows that the availability of premium fuel in 2017 for 12 months with the prediction results availability in January 2017 is 337490 kL and December 2017 is 344120 kL. It can be seen that the prediction results to be achieved fully met with a small error rate and the level of accuracy 83.33% where the results of the testing data showed the value of Mean Square Error for prediction the availability of premium fuel in 2017 is and also to the process of training produces the best network architecture with hidden layer 20 10 5 1 neurons and the best training algorithm by using learning rate 0.74 with MSE 0.00100. Thus, Backpropagation method is pretty well in predicting the availability of fuel.
Co-Authors E. R. Persulessy F. Kondolembang S. H. Saija Vicky Riyadi