International Journal of Advanced and Applied Sciences

Int. j. adv. appl. sci.

EISSN: 2313-3724

Print ISSN: 2313-626X

Volume 4, Issue 8  (August 2017), Pages:  19-28

Title:  Comparison of hybrid ANN models: A case study of instant noodle industry in Indonesia

Author(s):  Edy Fradinata 1, 2, *, Sakesun Suthummanon 2, Wannarat Sunthiamorntut 3


1Industrial Engineering, Universitas Serambi Mekkah, Banda Aceh, Indonesia
2Industrial Engineering, Prince of Songkla University, Hatyai, Thailand
3Computer Engineering, Prince of Songkla University, Hatyai, Thailand

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Artificial neural networks (ANNs) is the most stand popular practice to forecast the demand product since the other techniques still do not give the more accuracy. Furthermore, the hybrid method from ANN promises the best alternative to predict the customer demand. This paper proposes the hybrid model of ANN with the analytic hierarchy process (AHP), Monte Carlo (MC), and geometric random distribution to create new models to obtain the unusual methods in prediction. Those methods are substituted in the spaces of input weight and bias in the network. These hybrid methods are called AHPiwANNb and MCiwANNb. The hybrid technique of ANN has an approach of the time series-forecasting model. ANN is implemented in the testing case after the training process has run by the system, and process of validate is compared the testing from the training dataset. The Overall process is iterating the error to produce the mean squared error (MSE). The conclusions of this study, the hybrid ANN with AHP, MC and geometric random distributions show the good result of small MSE. More specifically, the hybrid AHPANN is better than hybrid MCANN. 

© 2017 The Authors. Published by IASE.

This is an open access article under the CC BY-NC-ND license (

Keywords: Hybrid method, Artificial Neural Networks, Analytic hierarchy process, Monte carlo, Geometric random, Forecasting model

Article History: Received 8 April 2017, Received in revised form 23 June 2017, Accepted 29 June 2017

Digital Object Identifier:


Fradinata E, Suthummanon S, and Sunthiamorntut W (2017). Comparison of hybrid ANN models: A case study of instant noodle industry in Indonesia. International Journal of Advanced and Applied Sciences, 4(8): 19-28


Block HD (1962). The perceptron: A model for brain functioning. i. Reviews of Modern Physics, 34(1): 123-134
BPS (2013). Proyeksi Penduduk Indonesia. Badan Pusat Statistik Indonesia. Jakarta, Indonesia. Avaialble online at:
Briggs AH, Goeree R, Blackhouse G, and O'Brien BJ (2002). Probabilistic analysis of cost-effectiveness models: choosing between treatment strategies for gastroesophageal reflux disease. Medical Decision Making, 22(4): 290-308.
Burke LI and Ignizio JP (1992). Neural networks and operations research: an overview. Computers and Operations Research, 19(3-4): 179-189.
Ciptomulyono U (2008). Fuzzy goal programming approach for deriving priority weights in the analytical hierarchy process (AHP) method. Journal of Applied Sciences Research, 4(2): 171-177.
Fradinata E, Suthummanon S, Sirivongpaisal N, and Suntiamorntuthq W (2014). ANN, ARIMA and MA timeseries model for forecasting in cement manufacturing industry: Case study at lafarge cement Indonesia—Aceh. In the International Conference of Advanced Informatics: Concept, Theory and Application, IEEE, Bandung, Indonesia: 39-44.
He H, Wang J, Graco W, and Hawkins S (1997). Application of neural networks to detection of medical fraud. Expert Systems with Applications, 13(4): 329-336.
Hemming C (2003). Using neural networks in linguistic resources. Department of Languages, University College of Skövde, Swedish National Graduate School of Language Technology, Skövde, Sweden. Available online at:
Karunanidhi K, Natarajan N, and Thangaraju P (1994). Genetic and Non-Genetic variation in birth placental weight and number of cotyledons in Mecheri and its Dorset cross sheep. Cheiron, 23: 255-260.
Kihoro J, Otieno R, and Wafula C (2004). Seasonal time series forecasting: A comparative study of ARIMA and ANN models. African Journal of Science and Technology, 5(2): 41-49
Kumar J and Roy N (2010). A hybrid method for vendor selection using neural network. International Journal of Computer Applications, 11(12): 35-40.
Kwok TY and Yeung DY (1997). Constructive algorithms for structure learning in feedforward neural networks for regression problems. IEEE Transactions on Neural Networks, 8(3): 630-645.
Law R (2000). Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting. Tourism Management, 21(4): 331-340.
Nolfi S and Parisi D (1996). Learning to adapt to changing environments in evolving neural networks. Adaptive Behavior, 5(1): 75-98.
Samuel OW, Asogbon GM, Sangaiah AK, Fang P, and Li G (2017). An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction. Expert Systems with Applications, 68: 163-172.
Santosa B (2007). Data mining teknik pemanfaatan data untuk keperluan bisnis. Graha Ilmu, Yogyakarta, Indonesia.
Satty TL (2004). Decision making—the analytic hierarchy and network processes (AHP/ANP). Journal of systems science and systems engineering, 13(1): 1-35.
Stam A and Kuula M (1991). Selecting a flexible manufacturing system using multiple criteria analysis. The International Journal of Production Research, 29(4): 803-820.
Stone M (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the royal statistical society: Series B (Methodological), 36(2): 111-147.
Tang SH, Hakim N, Khaksar W, Ariffin MKA, Sulaiman S, and Pah PS (2013). A hybrid method using analytic hierarchical process and artificial neural network for supplier selection. International Journal of Innovation, Management and Technology, 4(1): 109-111
Widrow B and Hoff ME (1960). Adaptive switching circuits. In the IRE WESCON Convention Record, 4(1): 96-104.