Vol. 5, No. 9, September 2024
E-ISSN:2723 – 6692
P-ISSN:2723– 6595
http://jiss.publikasiindonesia.id/
Journal of Indonesian Social Sciences, Vol. 5, No. 9, September 2024 2255
Tsukamoto Method of Fuzzy Logic for Hotel Quality
Determination
Azira Pertiwi, Febby Kesumaningtyas, Rifaldo Pratama, Ilham Eka Putra
Universitas Islam Sumatera Barat, Padang, Indonesia
Email: azirapertiwi[email protected], febbykesumaningtyas25@gmail.com, [email protected].id,
ilhamekaputra@uisb.ac.id
Correspondence: azirapertiwi04@gmail.com*
KEYWORDS
ABSTRACT
Analysis; Quality; Fuzzy
Tsukamoto
The rapid development of the hospitality industry has increased
competition among hotels to provide the best services and facilities
to attract customers. This study aims to determine hotel quality
using the Tsukamoto method of fuzzy logic. The research measures
the quality of hotel facilities, room classes, and prices, all of which
contribute to visitor comfort. Data were collected from 10 hotels and
processed using the Fuzzy Tsukamoto method to categorize hotel
quality as either high or low. The analysis showed that variables
such as hotel facilities and room class significantly influence the
overall quality rating. The results indicated that hotels with
complete facilities and higher room classes tend to score higher in
quality. This research demonstrates that the Tsukamoto method
provides an effective way to assess hotel quality based on multiple
variables.
Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Introduction
The hospitality industry has seen rapid growth in recent years, leading to increased
competition among hotels. According to the theory of service quality by Parasuraman et al. (1985) in
(Basri, 2019), the quality of services, particularly in the hospitality sector, is a significant determinant
of customer satisfaction and loyalty. This makes the evaluation of hotel quality crucial for staying
competitive. The application of fuzzy logic in service quality measurement offers a novel approach
that accounts for uncertainty and subjectivity in customer satisfaction metrics. This research aims to
provide a structured methodology using the Tsukamoto method of fuzzy logic, which allows for a
more nuanced and accurate determination of hotel quality. The urgency of this research is rooted in
the increasing need for reliable tools that can help hotels improve their services and remain
competitive in a saturated market. Hotel management often receives customer complaints about
unsatisfactory hotel quality. So that to find out the quality of a hotel, a research was carried out on
several hotels with several hotel facility needs.
To obtain research with appropriate results, the researcher uses the implementation of Fuzzy
using the Tsukamoto Method in determining the quality of a hotel with a standard value that has been
set for hotel determination by measuring the quality of facilities and room supplies that are able to
provide comfort for visitors.
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In real life, there are aspects in the real world that are always or usually outside of the
mathematical model and are Inexact. This concept of uncertainty is the basic concept of the
emergence of the concept of fuzzy logic (Abrori & Primahayu, 2015).
Fuzzy logic is one of the components that form soft computing. Fuzzy logic was first introduced
by Prof. Lotfi A. Zadeh in 1965. The basis of Fuzzy's Logic is Fuzzy's set theory. In Fuzzy's set theory,
the role of membership degree as a determinant of the existence of elements in a set is very important.
Membership value or degree of membership or membership is the main feature of the reasoning with
the Fuzzy Logic (Andrian, 2015).
Previous research has applied fuzzy methods in various contexts. For example, Sari and
Mahmudi (2015) used Fuzzy Tsukamoto method to determine the eligibility of prospective
employees, while Hayadi et al. (2016) applied this method to assess infant health and care. These
studies show that fuzzy methods are effective in handling situations involving many variables with
high levels of uncertainty. However, the application of fuzzy methods in the context of hotel quality
evaluation is still very limited.
This research seeks to fill the gap by applying the Tsukamoto Fuzzy method in determining
hotel quality. By considering variables such as room class, hotel facilities, and price, this study aims
to produce a rating system that can assist hotel management in making strategic decisions related to
improving service quality. This research also differs from previous studies because it does not only
focus on one aspect, such as facilities or price, but combines several important factors that affect
customer satisfaction. In this context, this research builds a new contribution by providing a more
comprehensive approach in determining hotel quality using the Fuzzy Tsukamoto method.
Thus, this research is expected to make a significant contribution to the literature related to the
application of fuzzy logic in the hospitality industry, as well as provide practical solutions for hotel
management in overcoming the challenges of quality assessment which is often subjective.
Materials and Methods
The research method can be explained in the form of a research framework This framework is
the steps that will be taken in solving the problem to be discussed. The framework of this research
can be illustrated in the following figure 1.
Problem Analysis
Defining Goals
Studying Literature
Problem Identification
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Figure 1. Research Framework
System Analysis and Design
This knowledge-based system contains Fuzzy criteria and sets for each criterion. These criteria
are classified into a set of variable languages for determining hotel quality as follows:
1. Room Class Type : Few and Many,
2. Hotel Facilities : Incomplete, Complete, and Very Complete
3. Price : Cheap, Medium, and Expensive
Table 1 is the data taken and observed directly at the research site, which will be presented and
analyzed using the Fuzzy Tsukomoto method.
Table 1 Hotel Data
No
Variable
Room Class
Type
Hotel Facilities
Price
1
4
3
Rp.200.000-Rp.450.000
2
5
6
Rp.500.000- Rp.1.000.000
3
4
4
Rp.150.000- Rp.450.000
4
5
5
Rp.400.000- Rp.700.000
5
3
2
Rp.200.000- Rp.400.000
6
4
2
Rp.150.000-Rp.450.000
7
3
3
Rp.150.000-Rp.450.000
8
6
7
Rp.450.000-Rp.1.000.000
9
4
4
Rp.350.000-Rp.500.000
10
6
9
Rp.500.000-Rp.1.000.000
The data will be processed using Fuzzy logic so that variables can be determined to get the
expected output.
Analyzing Data
Fuzzy Logic Implementation
Testing of Research Results
Collecting Data
Fuzzy Design
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Table 2 Input and Output Data
Function
Variable
Set
Input
Number of Room Class Types
Number of Hotel Facilities
Hotel Prices
Little, Many
Incomplete, Complete, Very
Complete
Cheap, Medium, Expensive
Output
Quality
Low, High
Table 3 Fuzzy Input Data
Input
Room Class
Hotel Facilities
Price
A Little
1-4
Many
3-6
Incomplate
1-6
Complate
4-8
Very Complate
6-10
Cheap
Rp.150.000-Rp.600.000
Medium
Rp.200.000-Rp.900.000
Expensive
Rp.600.000-Rp.1.000.000
In the Fuzzy input data table, there are 3 variables; the first is the Number of Room Class Types
that have a Few and Many sets, where the values ≥ 1 and ≤ 4, which means they enter the set few,
while the values ≥ 3 and ≤ 6 which means they enter the set of many. The second is Hotel Facilities,
which have a Complete, Incomplete and Very Complete set, where the values of ≥ 1 and ≤ 6 which
mean that they enter the Complete set, where the values of ≥ 4 and ≤ 8 mean they enter the Incomplete
set, while the values of ≥ 6 and ≤ 10 which means they enter the Very Complete set. The third is Hotel
Prices, which have a set of Cheap, Medium and Expensive, where the value of ≥Rp.150,000 and
≤Rp.600,000, which means it enters the Cheap set, where the value of ≥Rp.200,000 and ≤Rp.900,000
goes to the Medium set, while the value of ≥Rp.600,000 and ≤Rp.1,000,000 which means it enters the
Expensive set (Abdillah, 2015; Murti et al., 2015).
a. Function of Membership Degree
The membership degree function using the Tsukamoto Method is divided into membership, the
number of room class types, the number of hotel facilities, and hotel prices.
Table 4 Membership Function of Room Class Type,
Set
Domain
Few
1-4
Many
3-6
The number of Room Class Types that have a Few and Many sets, where the values ≥ 1 and ≤ 4,
which means they enter the set few, while the values ≥ 3 and ≤ 6, which means they enter the set of
many.
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0 1 2 3 4 5 6
Sedikit Banyak
Figure 2. Curve of Number of Room Types
The Slight set uses a membership function in the form of a descending curve, while the Majority
set uses a membership function in the form of an ascending curve.
Membership Function:
1; x 2
Little [x] = ( 4– x) / (4 – 2); 2 x 4
0; x ≥ 4
0; x 3
Many [x] (x – 3) / (5 – 3); 3 x 5
1; x ≥ 5
The variable set is Few [1-4], while the variable set Many has a domain [3-6].
Table 5 Functions of Hotel Facility Membership
Set
Domain
Incomplete
1-6
Complete
4-8
Very complete
6-10
The number of Hotel Facilities that have a Complete, Incomplete and Very Complete set, where
the values ≥ 1 and ≤ 6, which means they are in the Incomplete set, where the values ≥ 4 and ≤ 8 which
means they are in the Complete set, while the values ≥ 6 and ≤ 10 which means they are in the Very
Complete set.
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430 1 2 5
Tidak Lengkap Sangat Lengkap
6 7 8 9 10
Lengkap
Figure 3. Hotel Facility Curve
Incomplete Sets use a shoulder curve membership function, Complete Sets use a triangular
curve membership function, and very complete Sets use a shoulder curve membership function.
Membership Function:
1; x ≤ 4
µTidak Lengkap[x] = (8- x) / (8– 4); 4 ≤ x ≤ 8
0; x ≥ 8
0; x ≤ 4
µLengkap[x] = (x - 4) / (6 – 4); 4 ≤ x ≤ 6
(8 – x) / (8 – 6) 6 ≤ x ≤ 8
1; x=6
0; x ≤ 4
µSangat Lengkap[x] = (x – 4 ) / (6 – 4); 4 ≤ x ≤ 6
1; x ≥ 8
Incomplete variable sets have domains [1-6], Complete variable sets have domains [4-8],
while Very Complete variable sets have domains [6-10].
Table 6 Hotel Price Membership Function
Set
Domain
Cheap
Rp.150.000-Rp.600.000
Medium
Rp.200.000-Rp.900.000
Expensive
Rp.600.000-Rp.1.000.000
Hotel prices that have a set of Cheap, Medium and Expensive, where the value ≥ Rp.150,000
and ≤Rp. 600,000 which means it goes into the Cheap set, where the value is ≥Rp. 200,000 and ≤Rp.
900,000 goes to the Medium set, while the value of ≥600,000 and ≤ Rp.1,000,000 which means it goes
to the Expensive set.
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0150 200 450 500 600 700 800 900 1000
Murah Sedang Mahal
Figure 4. Hotel Price Curve
The Cheap Set uses a membership function in the form of a shoulder curve, the Medium Set
uses a membership function in the form of a triangle curve, and while the Expensive Set uses a
membership function in the form of a shoulder curve.
Membership Function:
1; x ≤ 200.000
µMurah[x] = (600.000- x) / (600.000– 200.000); 200.000≤ x ≤ 600.000
0; x ≥ 600.000
0; x ≤ 200.000
µSedang[x] = (x – 200.000) / (600.000 – 200.000); 200.000≤ x ≤ 600.000
(900.000 – x) / (900.000 – 600.000) 600.000 ≤ x ≤ 900.000
1; x=600.000
0; x ≤600.000
µMahal[x] = (x – 600.000 ) / (900.000 – 600.000); 600.000 ≤ x ≤ 1.000.000
1; x ≥ 900.000
The Variable Set is Cheap [Rp.150,000-Rp.600,000], the Medium variable set has a domain
[Rp.200,000-Rp.900,000], while the Expensive fuzzy set has a domain [Rp.600,000-Rp.1000,000].
Table 7 Quality Membership Functions
Set
Domain
Low
0-4
High
3-6
The Output results have Low and High values, where the values of ≥0 and ≤4 mean they enter
the Low set, while the values of ≥3 and ≤6 mean they have High values.
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0 1 2 3 4 5 6
Rendah Tinggi
Figure 5. Hotel Quality Curve
The Lower Set uses a membership function in the form of a descending curve, while the High
Set uses a membership function in the form of an ascending curve.
Membership Function:
1; x 2
Rendah [x] = ( 4– x) / (4– 2); 2 x 4
0; x ≥ 4
1; x 3
Tinggi [x] = ( x– 3) / (5 – 3); 3 x 5
0; x ≥ 5
The variable set is Low [0-4], while the variable set is High which has a domain [3-5].
Results and Discussions
Implementation and Results
The hotel data obtained is then grouped into 3 variables: the Number of Room Class Types, the
Number of Hotel Facilities, and the number of Hotel Prices. Table 8 shows the data taken for testing
this system, which includes as many as 10 hotels in the Painan area.
Table 8 Hotel Data
No
Name
Variable
Room Class
Type
Hotel Facilities
Price
1
Hotel Anordio
4
3
Rp.200.000 – Rp.450.000
2
Hotel Triza
5
6
Rp.500.000 – Rp.1.000.000
3
Hotel Andini
4
4
Rp.150.000 – Rp.450.000
4
Hotel Edotel
5
5
Rp.400.000 – Rp.700.000
5
Hotel Aroma
3
2
Rp.200.000 – Rp.400.000
6
Hotel Rihan
4
2
Rp.150.000 – Rp.450.000
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Hotel Adi Karya
3
3
Rp.150.000 – Rp.450.000
8
Hotel Giszella
6
7
Rp.450.000 – Rp.1.000.000
9
Hotel Muthia
4
4
Rp.350.000 – Rp.500.000
10
Hotel Langkisau
6
9
Rp.500.000 – Rp.1.000.000
The value of the Hotel Data Raw Data table will be processed into a table to calculate the
Number of Room Class Types, Number of Hotel Facilities and Hotel Prices.
a. Results of Hotel Data Definition
The results of this defuzzification can be in the form of variables such as the number of room
class types, number of hotel facilities, and hotel prices in achieving hotel quality. Whether the hotel
has Low or High quality, these 3 variables will produce a Hotel Quality Decision in the form of Low or
High based on the query described in the previous chapter (Amelia, 2013). Here, the results look like
those in Table 9.
Table 9 Display of Hotel Data Definition
No
Hotel
Name
Room Class Variable
Hotel Facility Variables
Room
Class
Few μ
Many μ
Hotel
Facilities
Incomplete
μ
Complete
μ
Very
Complete
μ
1
Anordio
4
0,00
0.50
3
0,75
0,25
0,00
2
Triza
5
0,00
1,00
6
0,00
0,00
1,00
3
Andini
4
0,00
0.50
4
1,00
0,00
0,00
4
Edotel
5
0,00
1,00
5
0,50
0,50
0,00
5
Aroma
3
0.50
0,00
2
0,88
0,13
0,00
6
Rihan
4
0,00
0.5,00
2
0,63
0,38
0,00
7
Adi Karya
3
0.50
0,00
3
1,00
0,00
0,00
8
Giszella
6
0,00
1,00
7
0.25
0,75
0,00
9
Muthia
4
0,00
0.50
4
0,50
0,00
0,00
10
Langkisau
6
0,00
1,00
9
0.25
0,75
0,00
Table 10 Hotel Data Defuzzyfication Display (Advanced)
No
Hotel Name
Price Variables
Hotel
Rating
Value
(z)
Hotel
Rating
Price
Cheap
μ
Medium
μ
Expensive
μ
1
Anordio
300000
0,75
0,25
0,00
2,25
Rendah
2
Triza
1000000
0,00
0,00
1,00
3,00
Tinggi
3
Andini
200000
1,00
0,00
0,00
2,50
Rendah
4
Edotel
400000
0,50
0,50
0,00
2,00
Rendah
5
Aroma
250000
0,88
0,13
0,00
2,38
Rendah
6
Rihan
350000
0,63
0,38
0,00
2,13
Rendah
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Adi Karya
200000
1,00
0,00
0,00
2,50
Rendah
8
Giszella
500000
0.25
0,75
0,00
2,75
Tinggi
9
Muthia
400000
0,50
0,00
0,00
2,00
Rendah
10
Langkisau
500000
0.25
0,75
0,00
2,75
Tinggi
The results of analyzing data from 10 hotels using the Fuzzy Tsukamoto method show that the
variables of room class and hotel facilities play a significant role in determining hotel quality. The raw
data that includes room class, facilities, and price are categorized in fuzzy sets for the fuzzification
process.
The data processing results show that hotels with more facilities and more room classes tend
to get higher quality scores. For example, Hotel Triza, which has the most number of room classes and
very complete facilities, gets a high quality score. In contrast, a hotel like Hotel Aroma, which has
fewer facilities and room classes, is categorized as a low-quality hotel. In addition, price also affects
hotel quality, but the effect is not as great as facilities and room classes (Fuady & Zulisa, 2023).
System Implementation
a) Main Page View
The main page view shows the main page view. The appearance is as shown in figure 6
Figure 6. Main Page Display
On the main page view there is a hotel data menu, how to determine hotel quality and see hotel
quality.
b) Page View of Add Hotel Data
In this new data addition view, we can enter new hotel data and go directly to the latest data
of Fuzzy's calculation.
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Figure 7. Page View of Add Hotel Data
On the page view, add hotel data can input new data containing hotel name, room class,
facilities and hotel prices.
c) Hotel Quality Calculation Page View
This is the end result of a hotel quality calculation that has low and high inputs
Figure 8. View of Hotel Quality Calculation Page
On the display of the hotel quality calculation page, this is the final result of the calculation
of 3 variables that have the final result of hotel quality.
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The results of this study are in line with previous research that emphasizes the importance of
service quality and facilities in determining customer satisfaction (Parasuraman et al., 1988) (Ali et
al., 2021; Omar et al., 2016). In this case, the use of the Fuzzy Tsukamoto method demonstrates
effectiveness in overcoming the uncertainty of quality assessment, as well as incorporating several
variables that influence customer perceptions of hotel quality.
However, this study also makes an important new contribution by introducing the Fuzzy
Tsukamoto method in the context of hotel quality evaluation, which has not been widely explored in
the existing literature. Previous studies using fuzzy logic were mostly applied in different fields, such
as employee eligibility determination Sari and Mahmudy, (2015) and infant health) Hayadi et al.,
(2016). Therefore, this research expands the scope of fuzzy application in the hospitality sector (Ali
et al., 2021; Doborjeh et al., 2022; Horng et al., 2018).
Research Gap
Although the Fuzzy Tsukamoto method has been widely used in other fields, such as employee
eligibility assessment or health care, this study fills a research gap in the context of hotel quality
assessment. Most of the previous research related to hotel quality still uses traditional methods,
which tend to be less effective in handling the uncertainty and subjectivity inherent in variables such
as facilities and customer comfort.
In addition, this study also fills a literature gap in terms of combining several important
variables-room class, amenities, and price-in one fuzzy-based rating system. Previous studies that
focused on one aspect, such as price or facilities, were unable to provide a comprehensive picture of
the factors that determine overall hotel quality. The Tsukamoto Fuzzy Method provides a holistic
approach in evaluating hotel quality, by considering multiple variables simultaneously.
Critical Evaluation
Although the results of this study show that room class and hotel facilities are the dominant
factors in determining hotel quality, these results need to be further evaluated with a larger sample.
Only 10 hotels were involved in this study, which may not be enough to provide a strong
generalization across the hospitality industry in Indonesia. In addition, although this study
successfully demonstrated the effectiveness of the Fuzzy Tsukamoto method, there are challenges in
applying more complex algorithms for broader data-driven decision-making (Lu et al., 2019; Wang et
al., 2021).
Future research could expand the scope by increasing the number of hotels studied and
incorporating other variables, such as online reputation or customer ratings on digital platforms, to
provide a more complete understanding of the factors that influence hotel quality.
Conclusion
In this study, 3 Fuzzy input variables were used: the Number of Hotel Class Types, the Number
of Hotel Facility Types, and Hotel Prices. To determine Hotel Quality, calculations are made using the
largest value. To analyze the testing of this research, a PHP program is used to determine queries in
database processing.
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