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

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  • 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
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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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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Articles