View of Post-Pandemic Hotel Decision Criteria Analysis Using Decision Making Methods

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Journal of Information Technology and Computer Science Volume 7, Number 1, April 2022, pp. 94-109

Journal Homepage: www.jitecs.ub.ac.id

Post-Pandemic Hotel Decision Criteria Analysis Using Decision Making Methods

Prima Melati Sukma¹, Cutifa Safitri*²

1,2Faculty of Computing, President University, Bekasi, Cikarang, Indonesia

1[email protected], ²[email protected]

*Corresponding Author

Received 07 March 2022; accepted 30 April 2022

Abstract. This paper proposes an optimized hybrid process of the Analytic Hierarchy Process with the Simple Additive Weighting method for hotel decision-making. This study is important as many sectors including tourism are striving in the post-pandemic era. The proposed hypothesis is proven through a study case of hotel selection which included four factors for the criteria in decision-making, namely price, facilities, class, and location. The supported literature review on the topic described both methods are still widely used in the decision-making process. This study critically analyzed the importance of the selected factors. The superiority of this approach is to measure the validity by considering the depreciated value. To validate our findings, a group of sampling is done by performing the hybrid methods. Calculated results revealed the proposed methods achieve the decision-making process, and the hybrid AHP – SAW model was found to be an effective method for assessing the hotel selection process.

1 Introduction

In everyday life, people make choices in different situations such as selecting a good place for dinner, buying the most suitable laptops, deciding on a place for vacation, or even choosing movies. Occasionally, making decisions will always be tough since weighing the alternatives requires time and effort. Also, each decision has both positive and negative effects. The human mind is incapable of considering all of the causes and consequences at the same time. People currently deal with these issues using instincts or mathematical models based on unverifiable assumptions that lead to conclusions that may or may not be useful [1]. In this technology era, decision-making is facilitated by a diverse selection of technologies. A Decision Support System (DSS) is one of the computerized programs used by decision-makers to find the best alternative. A Decision Support System is being developed to assist decision-makers in coping with complicated issue situations during the decision-making process. According to Power, DSS also has been used in a variety of research areas [2].

Currently, the world is facing the challenges of the pandemic. Many sectors are struggling during the post-pandemic era including the tourism area. Previously, hotels were permanently closed during the pandemic. Hence, now all sectors need to quickly adapt to the new normal. However, customers especially tourists can support the tourism fields to survive the global crisis. Customers might help by carrying out a staycation, which has become a trend in this post-pandemic era. Following the health

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Prima Melati et al. , Post-Pandemic Hotel Decision: ... 95 protocols, customers are able to decide and book the best hotel to stay in. The stated study case of hotel selection in the post-pandemic will assist the decision-making process. Moreover, the decision support system is one of the systems which are able to help the business factors, especially in the tourism area.

During the decision-making, a large number of alternatives might occur. The number of options accessible in decision-making demands the use of a simple method of calculation. Finding the best answer or organizing a list of issues is frequently done using the multi-criteria decision analysis approach. The objective of a multi-criteria decision-making issue is often to discover a solution to a specific set of alternatives.

Problems can occur when attributes of one or more choice variants change, or when new evaluation alternatives appear [3]. The Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW) techniques are two approaches that are still widely used.

The main purpose of the AHP technique is to compare the importance of the criteria that are used to make decisions [7]. While the SAW method is able to choose the best option from a large amount of alternatives depending on the specified criteria. Based on all of the used attributes, the SAW methods will create a final value consisting of the weighted sum of the performance ratings for each alternative [8]. The significance of this study is to give an overview of factors that are important in hotel decision- making, especially during the post-pandemic era, and elaborate further on the importance of these factors through a mathematical calculation using AHP and SAW techniques. Both techniques are combined and evaluated to determine the accuracy of each method.

2 Literature Review

The Analytic Hierarchy Process (AHP) implementation may be found in group decision-making which combines the decision-making system with the AHP approach. The goal of this study is to help decision-makers in analyzing the quality of gemstones. The AHP model was utilized to resolve problems with the knowledge of individuals and the capacity to assess the quality of gemstones. The AHP can tackle the said problem and gain suffusion improvement in solving the selection of quality gemstones [6]. In [7], the author mentioned that agreement is sometimes impossible to achieve without the commitment of the instrument that supports the decision-making process of selecting the best high-voltage substation technology.

To tackle the issue, the authors developed a new methodology built on the AHP method to assist in choosing between two popular alternatives, the Air Insulated Substation (AIS) and Gas Insulated Substation, for high voltage substation technology (GIS). The AHP is relevant for the stated problem and it specifies that the GIS technology is strongly favored.

Another study elaborated on the Simple Additive Weighting (SAW) method which uses a multi-criteria decision process to resolve personnel selection issues.

The Multi-Criteria Decision Making (MCDM) approach was shown using data from an actual case of the Iran telecommunications industry. For picking the best among five personnel and rating them, seven qualitative and positive criteria are used for the application [8]. The SAW technique is also used as an intelligent agent for monitoring the health of urban forests [9]. The problem in this paper is creating an intelligent agent to keep monitoring the forest health which leads the urban forest sustainability. Researchers provide analysis and design of intelligent agents as decision-makers in urban forest health monitoring, using the SAW model to address the problem and support the decision-maker [9].

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96 JITeCS Volume 7, Number 1, April 2022, pp 94-109 The last paper studies the comparison of SAW and knowledge-based SAW in recipe recommendation systems. Researchers compared both methods using correlation testing. The result shows that there is a strong correlation between knowledge-based SAW and user preference [10].

Table 1. Literature Summary of Studies in Hotel Criteria Evaluation

Refer ences

Factors Pr

ice Faci lities

Cl as s

Distance to Public Transportation

Loc atio n

Ra tin g

Breakfast Facilities

Wifi Faciliti

es Ser vice s

Clean liness

Bed Qualit

y

[16] x x x x

[17] x x x x

[18] x x x x

[19] x x x

[20] x x x x x

[21] x x x x x x

[22] x x x x

[23] x x x

[24] x x x x x x

[25] x x x x

Total 10 9 7 2 6 1 1 1 3 2 1

Table 1 shows the number of factors or criteria used by several researchers’

papers gathered for the AHP and SAW method with the study case of hotel selection from the period of 2013 to 2020. The papers are selected based on those researches that are relevant to our studies which are hotel selection in a certain city. The most used criteria are the price, followed by the facilities, class, and location.

The chosen factors are also considered with the situation of the post-pandemic, especially the location factor. The location criteria are one of the most important aspects of the hotel. During the post-pandemic, several locations are highlighted as the risk zone. Hence, the chosen hotels must be located in the safest zone. Facilities are also considered as to whether the hotels follow the health protocols and maintain hygiene or not.

3 Methodology

3.1 Analytic Hierarchy Process

Thomas Saaty established the Analytic Hierarchy Process (AHP), a decision- making technique that assists decision-makers set priorities and making the best decision. Personal judgments and values are logically combined in AHP. AHP can solve complex multi-criteria problems in a hierarchy [11]. While at the Wharton School, University of Pennsylvania, T. L. Saaty developed the AHP method in the years 1971 – 1975.

In general, the method consists of three main processes [12] which include identifying and organizing decision objectives, evaluating the pairwise comparisons, and synthesizing the results of the pairwise comparison. The AHP also includes a useful technique for ensuring the consistency of decision-makers' judgment and thereby eliminating decision-making bias.

The first step of the AHP method is to identify and organize decision objectives, criteria, and alternatives. The problem for the decision-making is defined along with the criteria and sought knowledge. Criteria play a vital role in every stage of the decision-making process.

The next step is to evaluate a pairwise comparison matrix. The pairwise comparison is important for the use of AHP. Generally, the AHP method is

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Prima Melati et al. , Post-Pandemic Hotel Decision: ... 97 mathematically formulated using a matrix. To develop a pairwise comparison matrix, the priorities for the main criteria should first be established. It is done by judging the criteria in pairs for relative importance. The numbers taken from the fundamental scale are the used judgments to make the comparison. A scale of 1 to 9 is utilized for the pairwise comparison judgments, the fundamental scale is shown in Table 2.

The next step is to evaluate a pairwise comparison matrix. The pairwise comparison is important for the use of AHP. Generally, the AHP method is mathematically formulated using a matrix. To develop a pairwise comparison matrix, the priorities for the main criteria should first be established. It is done by judging the criteria in pairs for relative importance. The numbers taken from the fundamental scale are the used judgments to make the comparison. A scale of 1 to 9 is utilized for the pairwise comparison judgments, the fundamental scale is shown in Table 2 as referred from Saaty in 1980 [15].

Table 2. Saaty’s Fundamental Scale

Scale Definition Rationale

1 Equal Importance The goal is equally benefited by the two activities.

3 Moderate importance of one over another

One activity is greatly preferred over another by experience and judgment.

5 Essential or strong importance One activity is greatly preferred over another by experience and judgment.

7 Very strong importance Activity is strongly favored and its dominance is demonstrated in practice.

9 Extreme importance The evidence favoring one activity over another is of the highest possible order of affirmation.

2, 4, 6, 8 Intermediate values between the two adjacent judgments

When compromise is needed.

The pairwise comparison result will be formed into a comparison matrix from the judgments The pairwise comparison matrix which includes the elements, alternatives criteria is shown in Table 3.

Table 3. Pairwise Comparison Matrix C1 C2 …. Cn

A1

….

An

A11

….

An1

A12

….

An2

…

….

A1n

… Ann

The AHP then adds the scores and the weights of the criteria. The next step will be to normalize the options and criteria after the pairwise comparison matrix has been created. The weight vector, also known as the priority vector or the eigenvalue normalization principle, was created through the normalizing procedure.

To avoid inconsistencies of the calculation, a consistency calculation will be done.

For the consistency index calculation, the maximum Eigen must first be calculated. To calculate the maximum Eigen or Lambda Max (λmax), the elements of the weighted sum matrices are divided by the priority vector of each criterion, and the average of the results is calculated to obtain the lambda max. Then, to maintain consistency, the consistency index (Cl) is calculated with (1).

𝐶𝐼 = ^^{max − 𝑛}

𝑛−1

(1)

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98 JITeCS Volume 7, Number 1, April 2022, pp 94-109 𝐶𝐼 : Consistency Index

𝑚𝑎𝑥 : maximum eigen n : number of criteria

The consistency ratio (CR) is calculated as well during this step, (2) is used.

𝐶𝑅 = ^𝐶𝐼

𝑅𝐼 (2)

𝐶𝐼 : Consistency Index 𝐶𝑅 : Consistency Ratio 𝑅𝐼: Random Index

The judgments are acceptable when the value of the consistency ratio is less than 0.1. Saaty stated that a Consistency Ratio of more than 0.1 indicates that the judgments are at the limit of consistency [13]. The alternatives and criteria then will be evaluated to obtain the final ranking. The last step helps decision-makers find answers for the best alternatives. However, if the results show inconsistency, the stages must be redone.

3.2 Simple Additive Weighting

The fundamental concept behind the Simple Additive Weighting (SAW) method originally forth by Zionts and Wallenius is to calculate a weighted total of the performance branches for each alternative attribute. The methodology also referred to as the weighted addition method, is frequently used as a multi-attribute decision- making method. The SAW technique implies normalizing the decision matrix to a scale that can be compared with all alternatives [14]. The SAW approach generally involves three steps, which include identifying the criteria to use as a guide for decision-making, deciding the suitability of each alternative, and assessing a decision matrix based on the criteria along with normalizing the matrix based on the equation. Based on all the used attributes, the SAW method will generate a final value that represents the total weighted performance rating of each alternative [5].

In the first step, decision-makers have to identify the alternatives and criteria as the reference for decision-making. As mentioned before, selecting the best hotel for tourists is the case for this paper. The main criteria used will be the price, facilities, class, and location of the hotels, while the alternatives will be a list of several hotels.

The criteria weight (W) is computed in the following step. The importance of the criteria in the decision-making process is indicated by the weight of the criteria. For each alternative on each criterion, a table of suitability ratings and decision matrix are also constructed.

After creating the table of suitability rating and decision matrix, normalize the conformity matrix that has been created. The equation utilized will be determined by the type of criteria which are the profit attribute or cost attribute. The result of this step is a normalized matrix. The normalization is carried out as follows:

𝑟𝑖𝑗 = {

𝑥_𝑖𝑗

𝑚𝑎𝑥_𝑖𝑗 , 𝑢𝑠𝑒𝑑 𝑖𝑓 𝑥 𝑖𝑠 𝑡ℎ𝑒 𝑏𝑒𝑛𝑒𝑓𝑖𝑡 𝑐𝑟𝑖𝑡𝑒𝑟𝑖𝑎 𝑚𝑖𝑛_𝑖𝑗

𝑥_𝑖𝑗 , 𝑢𝑠𝑒𝑑 𝑖𝑓 𝑥 𝑖𝑠 𝑡ℎ𝑒 𝑐𝑜𝑠𝑡 𝑐𝑟𝑖𝑡𝑒𝑟𝑖𝑎

(3) 𝑟_𝑖𝑗 : normalized performance rating value

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Prima Melati et al. , Post-Pandemic Hotel Decision: ... 99 𝑥_𝑖𝑗 : attribute value of each criterion

max (𝑥_𝑖𝑗) : maximum value of each criterion min ( 𝑥_𝑖𝑗) : minimum value of each criterion

The final step will be determined by adding the outcomes of the normalized matrix with the preference weight (W). The best alternative, which is the problem-solving option, is chosen based upon what option has the highest value. Equation (4) is a given formula as the preference value for each alternative (Vi). The maximum value of the rank of an alternative (Vi) indicates the best alternative.

𝑉_𝑖= ∑^𝑛_𝑗=1𝑊_𝑗𝑅_𝑖𝑗 (4) 𝑉𝑖 : rank of each alternative

𝑊_𝑗 : weightage value of each criterion 𝑅_𝑖𝑗 : normalized performance rating value 3.3 Hybrid Method for Decision Making

Fig. 1. Hybrid AHP – SAW Flowchart

Based on the identification of the proposed method, the results of the AHP criteria identification are capable to be the value scoring for the SAW method. The AHP method can identify the weightage criteria point. With the AHP method, each weightage criteria point can be defined accurately through pairwise comparison and normalization. However, the next process of decision-making will be determined by the SAW method. Hence, the combination of the AHP and SAW methods are used to

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100 JITeCS Volume 7, Number 1, April 2022, pp 94-109 achieve a better valuation in decision-making. The AHP method will perform the calculation of the weightage criteria along with the value of each alternative and the SAW method will perform the scoring of the criteria with the alternatives through normalization. A ranking process will be carried out as well to determine the best alternatives in the hotel selection. The hybrid structure of the method is depicted in Fig.

1.

Based on the figure of the hybrid method, the first step is to identify the most critical hotel decision criterion. The AHP then will perform the calculation of the criteria weightage through pairwise comparison and normalization. Further, the table of suitability and decision matrix will be evaluated by the SAW method, followed by normalizing the formed matrix. Normalization is done to get comparable input data using a common scale so that it can be compared with the alternatives. Equation (3) will be the SAW normalization formula. Then, the normalized matrix needs to be evaluated and pre-process to ensure the comparability of the criteria. Hence, the next step is to evaluate the normalized matrix with the preference weight. Finally, the best alternative is determined by the SAW method.

4 Result and Discussion

The scenario implemented in this paper is a study case of selecting hotels for tourists, which attracts the authors’ attention for the case study implementation.

The study case is able to help maintain the strive of tourism during the post- pandemic. Some factors are evaluated when choosing a hotel, especially in the post- pandemic era. However, this study focuses on four main factors which are price, facilities, class, and location.

The data used in this research was gathered using the web to obtain necessary information available of each of the evaluated hotels from the period of September 2021 until December 2021. The results of the implementation of each method and hybrid methods will be performed through a study case in which the input values are in the range of 1 to 9.

4.1 Analytic Hierarchy Process 4.1.1 Initialization Phase

The chosen study case contains four selected factors which are price, facilities, class, and location. The following inputted values of pairwise comparison with other factors are based on the preference of decision-makers. Decision-makers considered the factors in the post-pandemic situation. In this case, the price will be the most considered criterion for the decision-maker.

The result shows that the highest criterion weight was obtained by the price factor. After calculating the pairwise comparison, finding the matrix normalization of the criteria is the next step. Then, the consistency index is performed. The consistency index obtained from the calculation is 0.051, along with the forming of the consistency ratio. Then, the consistency ratio result shows 0.046. From the result, the author concludes that the results are consistent and the pairwise comparison is accepted.

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Prima Melati et al. , Post-Pandemic Hotel Decision: ... 101 Table 4. Initialization Phase of Criterion Results

4.1.2 Factor 1: Price

Further, the pairwise comparison and matrix normalization of the alternative is calculated. The first calculation starts with the hotels compared with the first factor, which is the price. The weightage criteria are determined as well during this phase. The calculation will then be followed by the facilities, class and location factors.

Table 5. Alternatives Compared with Price Results

Pairwise Comparison Result Alternatives Hotel A Hotel B Hotel C Hotel D

Hotel A 1 0.111 0.111 0.111

Hotel B 9 1 5 4

Hotel C 9 0.2 1 5

Hotel D 9 0.25 0.2 1

28 1.561 6.311 10.111

Matrix Normalization Result

Alternatives Hotel A Hotel B Hotel C Hotel D Alternatives Weight

Hotel A 0.036 0.071 0.018 0.011 0.034

Hotel B 0.321 0.641 0.792 0.396 0.538

Hotel C 0.321 0.128 0.158 0.495 0.275

Hotel D 0.321 0.16 0.032 0.099 0.153

4.1.3 Factor 2: Facilities

Table 6. Alternatives Compared with Facilities Results

Pairwise Comparison Result Alternatives Hotel A Hotel B Hotel C Hotel D

Hotel A 1 9 8 8

Hotel B 0.111 1 8 0.111

Hotel C 0.125 0.125 1 0.125

Hotel D 0.125 9 8 1

1.361 19.125 25 9.236

Matrix Normalization Result Initialization Value

Factor / Criterion Price Facilities Class Location

Price 1 1 5 9

Facilities 1 1 3 9

Class 0.2 0.333 1 5

Location 0.111 0.111 0.2 1

2.311 2.444 9.2 24

Factor / Criterion Price Facilities Class Location Criteria Weight

Price 0.433 0.409 0.543 0.375 0.44

Facilities 0.433 0.409 0.326 0.375 0.386

Class 0.087 0.136 0.109 0.208 0.135

Location 0.048 0.045 0.022 0.042 0.039

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102 JITeCS Volume 7, Number 1, April 2022, pp 94-109

Hotel A 0.735 0.471 0.32 0.866 0.598

Hotel B 0.082 0.052 0.32 0.012 0.116

Hotel C 0.092 0.007 0.04 0.014 0.038

Hotel D 0.092 0.471 0.32 0.108 0.248

4.1.4 Factor 3: Class

Table 7. Alternatives Compared with Class Results Pairwise Comparison Result

Alternatives Hotel A Hotel B Hotel C Hotel D

Hotel A 1 8 8 5

Hotel B 0.125 1 5 0.125

Hotel C 0.125 0.2 1 0.125

Hotel D 0.2 8 8 1

1.45 17.2 22 6.25

Hotel A 0.69 0.465 0.364 0.8 0.58

Hotel B 0.086 0.058 0.227 0.02 0.098

Hotel C 0.086 0.012 0.045 0.02 0.041

Hotel D 0.138 0.465 0.365 0.16 0.282

4.1.5 Factor 4: Location

Table 8. Alternatives Compared with Location Results Pairwise Comparison Result

Hotel A 1 0.125 1 0.2

Hotel B 8 1 8 8

Hotel C 1 0.125 1 0.2

Hotel D 5 0.125 5 1

15 1.375 15 9.4

Hotel A 0.067 0.091 0.067 0.021 0.062

Hotel B 0.533 0.727 0.533 0.851 0.661

Hotel C 0.067 0.091 0.067 0.021 0.062

Hotel D 0.333 0.091 0.333 0.106 0.216

4.1.6 Final Alternatives Comparison

Table 9. Final Alternatives Comparison Alternatives Pairwise Comparison

Hotel A Hotel B Hotel C Hotel D

Price 28 1.561 6.311 10.111

Facilities 1.361 19.125 25 9.236

Class 1.45 17.2 22 6.25

Location 15 1.375 15 9.4

Priority Vector AHP

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Prima Melati et al. , Post-Pandemic Hotel Decision: ... 103

Price 0.034 0.538 0.275 0.153

Facilities 0.598 0.116 0.038 0.248

Class 0.58 0.098 0.041 0.282

Location 0.062 0.661 0.062 0.216

4.1.7 Final Result of Analytic Hierarchy Process

From the above calculation, obtained the result of the best alternative for hotel selection with the AHP method. The final results of AHP show that the best alternative is won by Hotel B with a score of 0.323.

Table 10. Final Results of AHP Alternative Score Rank

Hotel A 0.266 4

Hotel B 0.323 1

Hotel C 0.31 2

Hotel D 0.299 3

4.2 Simple Additive Weighting 4.2.1 Initialization Phase

The first step of the Simple Additive Weighting Method is to define each factor. Each factor is defined as cost or benefit based on the type. In this case, the price factor has the cost type, while the facilities, class, and location are benefit types.

4.2.2 Factors Value

The preference weight of criteria in this study is W = (9, 8, 7, 5). In this case, the price will have the highest value of weightage, which means it is the most important factor among the others. Then, followed by the facilities, class, and location.

Table 11. Factors Value

4.2.3 Alternatives Value

The value of each alternative is defined based on the values determined previously. The input values are based on the research of hotel profiles through a real-time website.

Price Facilities Value

Price >= 2.000.000 Very Inadequate 4

1.000.000 <= Price < 2.000.000 Inadequate 5

800.000 <= Price < 1.000.000 Sufficient Adequate 7 600.000 <= Price < 400.000

Price <= 400.000

Adequate Very Adequate

8 9 Criteria Values of Class and Location

Class Location Value

1 Star Hotel Not Very Strategic 4

2 Stars Hotel Not Strategic 5

3 Stars Hotel Sufficient Strategic 7

4 Stars Hotel 5 Stars Hotel

Strategic Very Strategic

8 9

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104 JITeCS Volume 7, Number 1, April 2022, pp 94-109 Table 12. Alternatives Value

Price Facilities Class Location

Hotel A 4 8 7 4

Hotel B 9 7 7 8

Hotel C 9 8 8 9

Hotel D 8 7 9 9

4.2.4 Final Results of Simple Additive Weighting

The final result of the SAW calculation shows that the highest rank is obtained by hotel A.

Table 13. Final Results of SAW Alternative Score Rank

Hotel A 24.667 1

Hotel B 20.889 4

Hotel C 23.222 3

Hotel D 23.500 2

4.3 Hybrid AHP and SAW

The next step will be the evaluation of the hybrid Analytic Hierarchy Process and Simple Additive Weighting. Based on the evaluation of the individual calculation of each Analytic Hierarchy Process and Simple Additive Weighting, achieve that the Analytic Hierarchy Process can perform the weightage calculation more accurately and consistently than the Simple Additive Weighting method.

4.3.1 Analytic Hierarchy Process 4.3.2 Initialization Phase

The pairwise comparison and the normalization result of the main criteria are calculated with the result shown in Table 14.

Table 14. Initialization Phase Pairwise Comparison Result Factor / Criterion Price Facilities Class Location

Price 1 1 5 9

Facilities 1 1 3 9

Class 0.2 0.333 1 5

Location 0.111 0.111 0.2 1

2.311 2.444 9.2 24

Factor / Criterion Price Facilities Class Location Criteria Weight

Price 0.433 0.409 0.543 0.375 0.44

Facilities 0.433 0.409 0.326 0.375 0.386

Class 0.087 0.136 0.109 0.208 0.135

Location 0.048 0.045 0.022 0.042 0.039

4.3.3 Factor 1: Price

Table 15. Pairwise Comparison and Matrix Normalization of Price Pairwise Comparison Result

Hotel A 1 0.111 0.111 0.111

Hotel B 9 1 5 4

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Prima Melati et al. , Post-Pandemic Hotel Decision: ... 105

Pairwise Comparison Result

Hotel C 9 0.2 1 5

Hotel D 9 0.25 0.2 1

28 1.561 6.311 10.111

Hotel A 0.036 0.071 0.018 0.011 0.034

Hotel B 0.321 0.641 0.792 0.396 0.538

Hotel C 0.321 0.128 0.158 0.495 0.275

Hotel D 0.321 0.16 0.032 0.099 0.153

4.3.4 Factor 2: Facilities

Table 16. Pairwise Comparison and Matrix Normalization of Facilities Pairwise Comparison Result

Hotel A 1 9 8 8

Hotel B 0.111 1 8 0.111

Hotel C 0.125 0.125 1 0.125

Hotel D 0.125 9 8 1

1.361 19.125 25 9.236

Hotel A 0.735 0.471 0.32 0.866 0.598

Hotel B 0.082 0.052 0.32 0.012 0.116

Hotel C 0.092 0.007 0.04 0.014 0.038

Hotel D 0.092 0.471 0.32 0.108 0.248

4.3.5 Factor 3: Class

Table 17. Pairwise Comparison and Matrix Normalization of Class Pairwise Comparison Result

Hotel A 1 8 8 5

Hotel B 0.125 1 5 0.125

Hotel C 0.125 0.2 1 0.125

Hotel D 0.2 8 8 1

1.45 17.2 22 6.25

Hotel A 0.69 0.465 0.364 0.8 0.58

Hotel B 0.086 0.058 0.227 0.02 0.098

Hotel C 0.086 0.012 0.045 0.02 0.041

Hotel D 0.138 0.465 0.365 0.16 0.282

4.3.6 Factor 4: Location

Table 18. Pairwise Comparison and Matrix Normalization of Location Pairwise Comparison Result

Hotel A 1 0.125 1 0.2

Hotel B 8 1 8 8

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106 JITeCS Volume 7, Number 1, April 2022, pp 94-109

Pairwise Comparison Result

Hotel C 1 0.125 1 0.2

Hotel D 5 0.125 5 1

15 1.375 15 9.4

Hotel A 0.067 0.091 0.067 0.021 0.062

Hotel B 0.533 0.727 0.533 0.851 0.661

Hotel C 0.067 0.091 0.067 0.021 0.062

Hotel D 0.333 0.091 0.333 0.106 0.216

4.3.7 Final Alternatives Comparison

The final alternatives comparison as summarized in Table 18. The table shows the summary of the calculation results from price, facilities, class, and location. The values are taken from the result of the previous calculation in the AHP method.

Table 19. Final Alternatives Comparison Alternatives Pairwise Comparison

Price 28 1.561 6.311 10.111

Facilities 1.361 19.125 25 9.236

Class 1.45 17.2 22 6.25

Location 15 1.375 15 9.4

Priority Vector AHP

Price 0.034 0.538 0.275 0.153

Facilities 0.598 0.116 0.038 0.248

Class 0.58 0.098 0.041 0.282

Location 0.062 0.661 0.062 0.216

4.3.8 Simple Additive Weighting Based on Analytic Hierarchy Process The next process will be resumed by the SAW method. The weightage value obtained by the AHP method will be input and performed using the SAW method.

However, the first step of the SAW method is that the criteria type must be initialized.

4.3.9 Initialization

Depending on the type, each factor is classified as a cost or a benefit. While the facilities, class, and location are benefit types, the price factor has the cost type.

4.3.10 Factors Value

Further, the weightage preference is determined for each criterion in the SAW method. Since the hybrid method is implemented in this phase, the weightage preference of each criterion is obtained from the AHP calculation that has been done previously. The weightage criteria in AHP are gained through an initialization phase of AHP. The preference weight used for the criterion is W = (0.440, 0.386, 0.135, 0.039).

4.3.11 Alternative Values

The following values are used instead of the priority vector because the SAW

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Prima Melati et al. , Post-Pandemic Hotel Decision: ... 107 method will perform the matrix normalization. The table shows the value preference.

Table 20. Alternatives Value

Price 28 1.561 6.311 10.111

Facilities 1.361 19.125 25 9.236

Class 1.45 17.2 22 6.25

Location 15 1.375 15 9.4

4.3.12 Final Result of Hybrid AHP - SAW

In the final step, rank scores of each alternative are determined by the SAW method as summarized below. From the calculated result, concluded that the highest weightage factor falls under hotel B with a maximum score of 0.844.

Table 21. Final Results of Hybrid AHP – SAW Alternative Score Rank

Hotel A 0.093 4

Hotel B 0.844 1

Hotel C 0.669 2

Hotel D 0.275 3

4.1 Findings

The four factors which are the price, facilities, class, and location obtained different criteria weightage during the AHP and SAW calculation. Based on the calculation, the most important criterion considered especially during the post- pandemic era by the decision-makers in selecting hotels is the price. The importance then continued with the facilities, class, and location.

Based on the results, hotel B has the highest score calculated using the AHP method with a value of 0.323. Nevertheless, hotel B and Hotel C do not have a lot of points different from each other. The rank is then followed by hotels C, D, and A. The SAW method obtained hotel A as the best selection of hotels with a score of 24.667, followed by hotels D, C, and B. In reference to Hotel A with the highest price, the SAW method on the second case still does not perform the best result for the case of the decision-maker. However, during the Hybrid AHP-SAW, the best alternative is gained by Hotel B with a score of 0.844. From the final result of the hybrid AHP-SAW approach, hotel B is the best alternative out of the other hotels based on the case of the decision-maker. Also, based on the real-time research, hotel B is a three stars hotel that has an affordable price, sufficient adequate facilities that have become the second important criteria decided by decision-makers, and is located in a sufficient strategic location.

Table 22. Final Results of Study Case Alternative

Final Results

AHP Final Results SAW Final Results Hybrid AHP - SAW

Score Rank Score Rank Score Rank

Hotel A 0.266 4 24.667 1 0.093 4

Hotel B 0.323 1 20.889 4 0.844 1

Hotel C 0.31 2 23.222 3 0.669 2

Hotel D 0.299 3 23.500 2 0.275 3

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108 JITeCS Volume 7, Number 1, April 2022, pp 94-109 In conclusion, the AHP method is one of the decision-making methods that are able to maintain the calculation of weightage using pairwise comparison. With the pairwise comparison, normalization process, and consistency check, the AHP method obtained a more accurate weightage value. Based on the calculation of the weightage criteria preference calculated in the AHP method along with the total value of each alternative, the values are able to be input and resumed by the SAW method. Then, the SAW method will assist the normalization process along with the ranking process of alternatives. Hence, the AHP and SAW method are able to be a hybrid AHP – SAW method that assists to finish the decision-making process effectively with an accurate result.

5 Conclusion and Future Works

The AHP and SAW method are able to achieve the decision-making process. The important criteria used in the hotel selection are price, facilities, class, and location. A table of criteria evaluation is created to analyze the most significant and considered criterion used. Due to the ability of AHP to identify the weightage preference, the preferences are performed as the value scoring for the SAW method. The hybrid AHP with the SAW method assists in a more accurate final result for the weightage and ranking process. The hybrid AHP – SAW model was found to be an effective method for evaluating the hotel selection process along with the other decision-making problems.

For further research, adding criteria and alternatives can be applied. Also, a dynamic weightage can be implemented in future research where the weightage point of the criteria can change based on the current situation. Last, there are many methods that can assist the decision-making process, this study suggests the implementation of other methods combinations to obtain a better and more accurate result for the decision- making process.

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