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Jurnal Informatika Upgris
Published by Universitas PGRI Semarang
ISSN : 24604801     EISSN : 24776645     DOI : -
Core Subject : Science,
Computer Science & IT
Journal of Informatics UPGRIS published since June 2015 with frequency 2 (two) times a year, ie in June and December. The editors receive scientific writings from lecturers, teachers and educational observers about the results of research, scientific studies and analysis and problem solving closely related to the field of Information Technology and Communications / Informatics.
Arjuna Subject : -
Articles 11 Documents
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Search results for , issue "Vol 3, No 2: Desember (2017)" : 11 Documents clear
PERAMALAN HARGA SAHAM MENGGUNAKAN JARINGAN SYARAF TIRUAN SECARA SUPERVISED LEARNING DENGAN ALGORITMA BACKPROPAGATION Eko Riyanto
Jurnal Informatika Upgris Vol 3, No 2: Desember (2017)
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jiu.v3i2.1899

Abstract

Stock price prediction is useful for investors to see how the prospects of a company's stock investment in the future. Stock price prediction can be used to anticipate the deviation of stock prices. It can also helps investors in decision making. Artificial Neural Networks do not require mathematical models but data from problems to be solved. Information is conveyed through the data, and the Artificial Neural Network filters the information through training. Therefore, Artificial Neural Network is appropriate to solve the problem of stock price prediction.            Learning method that will be used to predict stock price is Supervised Learning with Backpropagation algorithm. With this algorithm, networks can be trained using stock price data from the previous time, classify it and adjust network link weight as new input and forecast future stock prices. By using ANN, time series prediction is more accurate. After analyzing the problem of stock price movement system, the writer can know the pattern of what variables will be taken for further insert into the stock price forecasting system.            This application can be used for stock price forecasting technique, so it will be useful for beginner investor as well as advanced investor as reference to invest in capital market. Implementing supervised learning backpropagation method will get accurate forecasting results more than 98%.Keyword - artificial neural network, stock, backpropagation.

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