Use of Binary Sigmoid Function And Linear Identity In Artificial Neural Networks For Forecasting Population Density

Anjar Wanto(1*), Agus Perdana Windarto(2), Dedy Hartama(3), Iin Parlina(4),

(1) STIKOM Tunas Bangsa Pematangsiantar
(2) STIKOM Tunas Bangsa Pematangsiantar
(3) STIKOM Tunas Bangsa Pematangsiantar
(4) AMIK Tunas Bangsa Pematangsiantar
(*) Corresponding Author

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.


Full Text:

PDF

References


Solikhun, A. P. Windarto, Handrizal, and M.Fauzan, “Jaringan Saraf Tiruan Dalam Memprediksi Sukuk Negara Ritel Berdasarkan Kelompok Profesi Dengan Backpropogation Dalam Mendorong Laju Pertumbuhan Ekonomi,” Kumpulan jurnaL Ilmu Komputer (KLIK), vol. 4, no. 2, pp. 184–197, 2017.

S. Amalia, “Pengenalan Digit 0 Sampai Digit 9 Menggunakan Ekstraksi Ciri MFCC dan Jaringan Syaraf Tiruan Backpropagation,” Jurnal Teknik Elektro ITP, vol. 6, no. 1, pp. 1–14, 2017.

A. Triyono, A. J. Santoso, and Pranowo, “Penerapan Metode Jaringan Syaraf Tiruan Backpropagation Untuk Meramalkan Harga Saham (IHSG),” Jurnal Sistem Dan Informatika, vol. 11, pp. 165–172, 2016.

A. Wanto and A. P. Windarto, “Analisis Prediksi Indeks Harga Konsumen Berdasarkan Kelompok Kesehatan Dengan Menggunakan Metode Backpropagation,” Sinkron Jurnal & Penelitian Teknik Informatika, vol. 2, no. 2, pp. 37–44, 2017.

A. Rahman and N. R. Hamzah, “Posisi Penduduk Kota Makassar Dalam Menunjang Pembangunan Ekonomi,” Jurnal Economics, Social, and Development Studies (EccesS), vol. 4, no. 1, pp. 1–22, 2017.

N. A. T, A. Murnomo, and A. Suryanto, “Implementasi Neural Network pada Matlab untuk Prakiraan Konsumsi Beban Listrik Kabupaten Ponorogo Jawa Timur,” Jurnal Teknik Elektro, vol. 9, no. 1, pp. 7–12, 2017.

F. Novadiwanti, A. Buono, and A. Faqih, “Prediksi Awal Musim Hujan di Kabupaten Pacitan Menggunakan Optimasi Cascade Neural Network (CNN) dengan Genetic Algorithm (GA) Berdasarkan Data GCM,” Jurnal Tanah dan Iklim, vol. 41, no. 1, pp. 69–77, 2017.

M. Najwa, B. Warsito, and D. Ispriyanti, “Pemodelan jaringan syaraf tiruan dengan algoritma one step secant backpropagation dalam return kurs rupiah terhadap dolar amerika serikat,” Jurnal Gaussian, vol. 6, no. 1, pp. 61–70, 2017.

A. Novita, “Prediksi Pergerakan Harga Saham Pada Bank Terbesar Di Indonesia Dengan Metode Backpropagation Neural Network,” Jurnal Teknik Informatika dan Sistem Informasi (JUTISI), vol. 5, no. 1, pp. 965–972, 2016.

P. Hamet and J. Tremblay, “Artificial intelligence in medicine,” Metabolism: Clinical and Experimental, vol. 69, pp. 1–14, 2017.

Sumijan, A. P. Windarto, A. Muhammad, and Budiharjo, “Implementation of Neural Networks in Predicting the Understanding Level of Students Subject,” International Journal of Software Engineering and Its Applications, vol. 10, no. 10, pp. 189–204, 2016.

J. Adnan et al., “Multilayer Perceptron Based Activation Function On Heart Abnormality Activity,” Journal of Fundamental and Applied Sciences, vol. 9, pp. 417–432, 2017.

A. Ehret, D. Hochstuhl, D. Gianola, and G. Thaller, “Application of neural networks with back-propagation to genome-enabled prediction of complex traits in Holstein-Friesian and German Fleckvieh cattle,” Genetics Selection Evolution, vol. 47, no. 1, pp. 1–9, 2015.

Z.-H. Wang, D.-Y. Gong, X. Li, G.-T. Li, and D.-H. Zhang, “Prediction of bending force in the hot strip rolling process using artificial neural network and genetic algorithm (ANN-GA),” The International Journal of Advanced Manufacturing Technology, 2017.

D. Huang and Z. Wu, “Forecasting outpatient visits using empirical mode decomposition coupled with backpropagation artificial neural networks optimized by particle swarm optimization,” PLoS ONE, vol. 12, no. 2, pp. 1–18, 2017.

J. Tarigan, Nadia, R. Diedan, and Y. Suryana, “Plate Recognition Using Backpropagation Neural Network and Genetic Algorithm,” Procedia Computer Science, vol. 116, pp. 365–372, 2017.

F. X. Diebold, Forecasting in Economics, Business, Finance and Beyond. 2017.

C. Ugwu and O. C, “Machine Learning Application for Stock Market Prices Prediction,” IOSR Journal of Computer Engineering (IOSR-JCE), vol. 16, no. 5, pp. 112–122, 2014.

A. P. Windarto, L. S. Dewi, and D. Hartama, “Implementation of Artificial Intelligence in Predicting the Value of Indonesian Oil and Gas Exports With BP Algorithm,” International Journal of Recent Trends in Engineering & Research (IJRTER), vol. 3, no. 10, pp. 1–12, 2017.




DOI: https://doi.org/10.30645/ijistech.v1i1.6

Refbacks

  • There are currently no refbacks.







Jumlah Kunjungan:

View My Stats

Published Papers Indexed/Abstracted By: