Open AccessAnalysis of Artificial Neural Networks Method Backpropagation to Improve the Understanding Student in Algorithm and Programming
Author(s) -
Muhammad Ridwan Lubis,
Widodo Saputra,
Anjar Wanto,
Sundari Retno Andani,
P Poningsih
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1255/1/012032
Subject(s) - backpropagation , artificial neural network , computer science , process (computing) , artificial intelligence , algorithm , base (topology) , machine learning , mathematics , programming language , mathematical analysis
To Improve the student skills in learning is constitute hope of every lecturer. For/In computer base-study programme master of programming is needed. the Algorithms and Programming are basic course that must be studied. Mean while/that for, using the backpropogation methods which is part of the Artificial Neural Networks will test and train data by using the backpropogation algorithm to improve students ability to understand the subject. The result obteined can be analyzed about the results if the output is that the closed to the target then the trainning process can be finished. Furthermore/Hereafter, The average error can be calcutate obteined from the result of the trainning that has been carried out. If the results have not reached then the backpropagation method always do iteration with make to update weignt until maximal output and can to get output of exelent with used backpropagation method.