Data Modelling To Determine Room Rate with Adaptive Network Based Fuzzy Inference System And Particle Swarm Optimization

 Abstract views: 248

Authors

  • Fahd Sulthony Faculty of Industrial Engineering, Institute of Technology Adhi Tama Surabaya
  • Lukmandono Institut Teknologi Adi Tama Surabaya
  • Rony Prabowo Institut Teknologi Adi Tama Surabaya

DOI:

https://doi.org/10.36456/tibuana.3.02.2564.48-57

Keywords:

Data Modelling, Adaptive Network based Fuzzy Inference System, Particle Swarm Optimization, Room Rate.

Abstract

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.

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References

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.

https://doi.org/10.1016/j.tourman.2012.03.0

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

hal.397-402.

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

No.4.

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

,Guna Wijaya,Surabaya.

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Published

2020-07-29

How to Cite

Sulthony, F. ., Lukmandono, & Prabowo, R. . (2020). Data Modelling To Determine Room Rate with Adaptive Network Based Fuzzy Inference System And Particle Swarm Optimization. Tibuana, 3(02), 48–57. https://doi.org/10.36456/tibuana.3.02.2564.48-57

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