Data Modelling To Determine Room Rate with Adaptive Network Based Fuzzy Inference System And Particle Swarm OptimizationAbstract views: 172
Keywords:Data Modelling, Adaptive Network based Fuzzy Inference System, Particle Swarm Optimization, Room Rate.
Determination of room rate in a hotel is
influenced by two factors, namely internal and
external. From an external perspective, PT. PIM
has eight competitor hotels that affect its room
rate. The Hotel Manager analyzes each
competitor's room rate changes to stay
competitive. Human analysis has several
shortcomings: subjectivity, fatigue and
inconsistency. Then we need a decision support
or decision companion machine to determine the
room rate. ANFIS-PSO is a hybrid algorithm
from the Adaptive neural network based fuzzy
inference system (ANFIS) by utilizing Particle
Swarm Optimization (PSO) optimization.
Traditional ANFIS is Gradient Decent (GD) as
an algorithm for parameter optimization (model).
This often happens to be stuck to get optimal
local results, to overcome this PSO is used as a
solution. The results obtained from the ANFIS- PSO training contained a difference of Rp.
3173,187 or a percentage of 1.34%. From the
modeling obtained applied to the hotel PT.PIM,
with the result of an increase in revenue of Rp.
17,493,548. The conclusion obtained is that
ANFIS-PSO can help managers to determine the
room rate by modeling data obtained from the
ANFIS-PSO method. Suggestion for the
development of this research is that ANFIS-PSO
has a complex complexity of training algorithms
because there is a combination of two
algorithms, so to make it better a different
algorithm design is needed.
Abdullah, A. A., & Terengganu, K. (2012).
Internal Success Factor of Hotel Occupancy
Rate. International Journal of Business and
Social Sience, 3(22), 199–218.
Atsalakis, G. S., Atsalaki, I. G., &
Zopounidis, C. (2018). Forecasting the
success of a new tourism service by a neurofuzzy technique. European Journal of
Operational Research, 268(2), 716–727.
https://doi.org/10.1016/j.ejor.2018.01.044. 3. Al Shehhi, M., & Karathanasopoulos, A.
(2018). Forecasting Hotel Prices in Selected
Middle East and North Africa Region
(MENA) Cities with New Forecasting
Tools. Theoretical Economics Letters, 08(09), 1623–1638.
https://doi.org/10.4236/tel.2018.89104. 4. Becerra, M., Santaló, J., & Silva, R. (2013).
Being better vs. being different:
Differentiation, competition, and pricing
strategies in the Spanish hotel industry.
Tourism Management, 34, 71–79.
5. Bonner, J. (2017). New developments in
tourism and hotel demand modeling and
forecasting. International Journal of
Contemporary Hospitality Management, 29, 507–529. 6. Dong, Minggang., Wang, Ning. (2010),
“Adaptive Network-Based Fuzzy Inference
System with Leave-Out Cross-Validation
Approach for Prediction of Surface
Roughness”, Elsevier Jurnal Applied
Mathematical Modelling Vol.35 hal 1024- 1035.
Journal, I., & Issn, M. E. (2018). An
Adaptive Neuro-Fuzzy Inference System
(ANFIS) for Wire-EDM of Ballistic Grade
Aluminium Alloy. International Journal of
Automotive and Mechanical Engineering, 15(2), 5295–5307. 8. J-S.R. Jang. (1993) “ANFIS : Adaptive
Neural Network based Fuzzy Inference
System”, IEEE Transaction on System, MAN
and Cybernetics, Vol.23.
Lee, S. H. (2016). How hotel managers
decide to discount room rates: A conjoint
analysis. International Journal of
Hospitality Management, 52, 68–77.
https://doi.org/10.1016/j.ijhm.2015.09.014. 10. Rini, Dian Palupi., Shamsuddin, Siti
Mariyam., Yuhanis, Sophiayati Yuhanis.
(2014), “Particle Swarm Optimization for
ANFIS Interpretability and Accuracy”,
Springer Jurnal Soft Computing.
Pusinho, H.M.I., Mendes, V.M.F., Catalao,
J.P.S. (2010), “A Hybrid PSO-ANFIS
Approach for Short-Term Wind Power
Prediction in Portugas”, Elsevier Jurnal
Energy Conversion and Management Vol.52
Soleimani, M., Salmalian, K. (2012)
“Genetic Algoritm Optimized ANFIS
Network for Modeling and Rediction of
Energy Absorption rate of Brass Energy
Absorbers”, Journal of Applied Matematics, Islamic Azad University of Lahijan, Vol.8
Sagir, A. M., & Sathasivam, S. (2017). A
novel adaptive neuro fuzzy inference system
based classification model for heart disease
prediction. Pertanika Journal of Science and
Technology, 25(1), 43–56. 14. Santoso, Budi., Willy,Paul. (2011), Metoda
Metaheuristik konsep dan implementasi