Open Access Open Access  Restricted Access Subscription or Fee Access

Prediction of the Number of Airport Passengers Using Fuzzy C-Means and Adaptive Neuro Fuzzy Inference System

(*) Corresponding author

Authors' affiliations



Airport requires a system to predict the number of passengers as a reference for  airport development planning. In this study, the data used are time series of the number of passengers for eleven years. These data will form patterns which indicate the number of passengers each month in a year as the input data and the number of passengers next year as a target prediction. After the input data are clustered into three types using fuzzy C-means (FCM), the data are processed using adaptive neuro fuzzy inference system (ANFIS) to get the prediction data. The result shows that the “Mean Absolute Percentage Errors (MAPE ) which represent the errors for 4 years are  4.20%, 5.70%, 5.36% and 4.47%  with an average of 4.93% . Based on this result, FCM and ANFIS can be combined to predict the data time series.
Copyright © 2017 Praise Worthy Prize - All rights reserved.


Prediction; Number of Passenger; FCM; ANFIS

Full Text:



T. Litman and D. Burwell, “Issues in sustainable transportation,” Int. J. Glob. Environ. Issues, vol. 6, no. 4, pp. 331–347, 2006.

Z. Xie, L. Jia, Y. Qin, and L. Wang, “A hybrid temporal-spatio forecasting approach for passenger flow status in chinese high-speed railway transport hub,” Discret. Dyn. Nat. Soc., vol. 2013, 2013.

A. Wijaya and A. S. Girsang, “The Use of Data Mining for Prediction of Customer Loyalty,” CommIT J., vol. 10, no. 1, pp. 41–47, 2016.

L. Wang, Q. Zhang, Y. Cai, J. Zhang, and Q. Ma, “Simulation study of pedestrian flow in a station hall during the Spring Festival travel rush,” Phys. A Stat. Mech. its Appl., vol. 392, no. 10, pp. 2470–2478, 2013.

Santoso, I., Gulo, R., Girsang, A., An Adaptive Cat Swarm Optimization Based on Particle Swarm Optimization Approach (ACPSO) for Clustering, (2016) International Review on Computers and Software (IRECOS), 11 (1), pp. 20-26.

Indrawati, A., Girsang, A., Electricity Demand Forecasting Using Adaptive Neuro-Fuzzy Inference System and Particle Swarm Optimization, (2016) International Review of Automatic Control (IREACO), 9 (6), pp. 397-404.

Y. Zhang and J. Lei, “Prediction of Laser Cutting Roughness in Intelligent Manufacturing Mode Based on ANFIS,” Procedia Eng., vol. 174, pp. 82–89, 2017.

X. Ji, X. Zhou, and B. Ran, “A cell-based study on pedestrian acceleration and overtaking in a transfer station corridor,” Phys. A Stat. Mech. its Appl., vol. 392, no. 8, pp. 1828–1839, 2013.

N. Sevani and Yosua Jaya Chandra, “Web Based Application for Early Detection of Vitamin and Mineral Deficiency,” CommIT (Communication & Information Technology) Journal 10(2), 53–58, 2016.

G. Model, “Article in press,” 2016.

Lanre Olatomiwa, Saad Mekhilef, Shahaboddin Shamshirband, Dalibor Petković, Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria, Renewable and Sustainable Energy Reviews, Volume 51, 2015, Pages 1784-1791.

H. G. Lee, M. Piao, and Y. H. Shin, “Wnd power pattern forecasting based on projected clustering and classification methods,” ETRI J., vol. 37, no. 2, pp. 283–294, 2015.

M. Varedi, “Forecasting seat sales in passenger airlines: introducing the round-trip model,” Management, 2010.

Suharjito, S. Nanda, and B. Soewito, “Modeling software effort estimation using hybrid PSO-ANFIS,” in Proceeding - 2016 International Seminar on Intelligent Technology and Its Application, ISITIA 2016: Recent Trends in Intelligent Computational Technologies for Sustainable Energy, 2017.

A. M. Abdulshahed, A. P. Longstaff, and S. Fletcher, “The application of ANFIS prediction models for thermal error compensation on CNC machine tools,” Appl. Soft Comput. J., vol. 27, pp. 158–168, 2015.

L. Yang and E. Entchev, “Performance prediction of a hybrid microgeneration system using adaptive neuro-fuzzy inference system (ANFIS) technique,” Appl. Energy, vol. 134, pp. 197–203, 2014.

M. Legowo, N., Kanigoro, B., Salman, A. G., & Syafii, “Adaptive Neuro Fuzzy Inference System for Diagnosing Dengue Hemorrhagic Fever,” in Asian Conference on Intelligent Information and Database Systems, 2015, pp. 440–447.

Y. B. Napitupulu, T. A., & Wijaya, “Prediction Of Stock Price Using Artificial Neural Network: A Case Of Indonesia.,” J. Theor. Appl. Inf. Technol., vol. 54, no. 1, 2013.

M. Mirrashid, “Earthquake magnitude prediction by adaptive neurofuzzy inference system (ANFIS) based on fuzzy C-means algorithm,” Nat. Hazards, vol. 74, no. 3, pp. 1577–1593, 2014.

G. Shmueli, “To explain or to predict?,” Stat. Sci., vol. 25, pp. 289–310, 2010.

M. Abdollahzade, A. Miranian, H. Hassani, and H. Iranmanesh, “A new hybrid enhanced local linear neuro-fuzzy model based on the optimized singular spectrum analysis and its application for nonlinear and chaotic time series forecasting,” Inf. Sci. (Ny)., no. September, 2014.

J. D. Atkins, S. Y. Boateng, T. Sorensen, and L. J. McGuffin, “Disorder prediction methods, their applicability to different protein targets and their usefulness for guiding experimental studies,” International Journal of Molecular Sciences, vol. 16, no. 8. pp. 19040–19054, 2015.

H. Hindarto and S. Sumarno, “Feature Extraction of Electroencephalography Signals Using Fast Fourier Transform,” CommIT (Communication Inf. Technol. J., vol. 10, no. 2, p. 49, 2016.

M. O. F. Technology, “Anfis Based Data Rate Prediction for Cognitive Radio Anfis Based Data Rate Prediction,” 2010.

J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. 2012.

J. C. Bezdek, R. Ehrlich, and W. Full, “FCM: The fuzzy c-means clustering algorithm,” Comput. Geosci., vol. 10, no. 2–3, pp. 191–203, 1984.

H.-J. Zimmermann, Fuzzy Set Theory—and Its Applications. 2001.

J. Yen and R. Langari, Fuzzy Logic: Intelligence, Control, and Information. 1999.

S. Osowski, T. H. Linh, and K. Brudzewski, “Neuro-fuzzy TSK network for calibration of semiconductor sensor array for gas measurements,” IEEE Trans. Instrum. Meas., vol. 53, no. 3, pp. 630–637, 2004.

J.-S. R. Jang, “Neuro-fuzzy and soft computing for speaker recognition,” in Proceedings of 6th International Fuzzy Systems Conference, 1997, vol. 2, pp. 663–668.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2024 Praise Worthy Prize