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IJISTECH (International Journal Of Information System & Technology)
ISSN : 25807250     EISSN : -     DOI : -
Core Subject : Science,
IJISTECH (International Journal Of Information System & Technology) is published with both online and print versions. The journal covers the frontier issues in the computer science and their applications in business, industry and other subjects. The computer science is a branch of engineering science that studies computable processes and structures. It contains theories for understanding computing systems and methods; computational algorithms and tools; methodologies for testing of concepts. The subjects covered by the journal include artificial intelligence, bioinformatics, computational statistics, database, data mining, financial engineering, hardware systems, imaging engineering, internet computing, networking, scientific computing, software engineering, and their applications etc.
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Articles 1 Documents
Search results for , issue " Vol 1, No 1 (2017): November" : 1 Documents clear
Use of Binary Sigmoid Function And Linear Identity In Artificial Neural Networks For Forecasting Population Density Wanto, Anjar; Windarto, Agus Perdana; Hartama, Dedy; Parlina, Iin
IJISTECH (International Journal Of Information System & Technology) Vol 1, No 1 (2017): November
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1444.995 KB) | DOI: 10.30645/ijistech.v1i1.6

Abstract

Artificial Neural Network (ANN) is often used to solve forecasting cases. As in this study. The artificial neural network used is with backpropagation algorithm. The study focused on cases concerning overcrowding forecasting based District in Simalungun in Indonesia in 2010-2015. The data source comes from the Central Bureau of Statistics of Simalungun Regency. The population density forecasting its future will be processed using backpropagation algorithm focused on binary sigmoid function (logsig) and a linear function of identity (purelin) with 5 network architecture model used the 3-5-1, 3-10-1, 3-5 -10-1, 3-5-15-1 and 3-10-15-1. Results from 5 to architectural models using Neural Networks Backpropagation with binary sigmoid function and identity functions vary greatly, but the best is 3-5-1 models with an accuracy of 94%, MSE, and the epoch 0.0025448 6843 iterations. Thus, the use of binary sigmoid activation function (logsig) and the identity function (purelin) on Backpropagation Neural Networks for forecasting the population density is very good, as evidenced by the high accuracy results achieved.

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