Helping tourists find tourist destinations in accordance with the budget

Oct 13, 2025 by thesweetdesigns.com 96 views

Decision Supporting System for Determination of Tourist Purpose Using Fuzzy Logic by Clustering Method

Introduction

The tourism industry has experienced rapid growth in recent years, but it is often accompanied by inadequate technology to help tourists plan their trips. Many prospective tourists struggle to find tourist destinations that meet their needs and budget, particularly when it comes to choosing a destination that offers a combination of accommodation, culinary, and attractive activities at an affordable price. To address this issue, a decision support system is needed that can suggest various cost factors using a more flexible Fuzzy (fuzzy) method and in accordance with tourist preferences. This system can help prospective tourists minimize vacation costs and maximize their experience.

Background

The development of tourism has led to an increase in the number of tourists traveling to various destinations around the world. However, the lack of adequate technology to help tourists plan their trips has resulted in many prospective tourists finding it difficult to find tourist destinations that meet their needs and budget. This can lead to a range of problems, including:

Inefficient use of time: Prospective tourists may spend a significant amount of time researching and comparing different tourist destinations, which can be time-consuming and frustrating.
Increased costs: Prospective tourists may end up paying more for their vacation than they need to, particularly if they are not aware of the various cost factors involved.
Limited options: Prospective tourists may be limited in their options for tourist destinations, which can result in a less enjoyable vacation experience.

Methodology

To address these issues, a decision support system was developed using the Fuzzy C-Means (FCM) clustering method. This method is able to group data based on similar characteristics, such as the cost of hotel, restaurant, and tourist activities. The system was built using the Visual Basic 6.0 programming language and the MySQL database.

The clustering process was carried out with the following parameters:

Total cluster = 3: The data was grouped into three groups based on the cost level, namely cheap, medium, and expensive groups.
Weight = 2: The weight was given to the cost data to emphasize the results of clustering on the cost factor.
Maximum iteration = 100: The system will do iteration up to 100 times to get optimal clustering results.

The clustering process was carried out on the cost of hotel, restaurant, and tourist activities. After the data was grouped, the system adjusted it to tourist preferences, such as the length of vacation time and the available budget.

System Design

The decision support system was designed to provide comprehensive information about various choices of tourist destinations, including hotel, restaurant, and tourist activities. The system was able to suggest various cost factors using a more flexible Fuzzy method and in accordance with tourist preferences.

The system consisted of the following components:

Data collection: The system collected data on various cost factors, including hotel, restaurant, and tourist activities.
Clustering: The system used the FCM clustering method to group the data into three groups based on the cost level.
Adjustment: The system adjusted the grouped data to tourist preferences, such as the length of vacation time and the available budget.
Suggestion: The system suggested various cost factors using a more flexible Fuzzy method and in accordance with tourist preferences.

Advantages

The decision support system has several advantages, including:

Helping tourists find tourist destinations in accordance with the budget: The system helps prospective tourists find tourist destinations that are in accordance with their budget by combining various cost factors.
Facilitating the vacation planning process: The system can help prospective tourists choose tourist destinations that suit their needs and preferences easily and quickly.
Providing comprehensive information: The system provides information about various choices of tourist destinations, including hotel, restaurant, and tourist activities.

Conclusion

The decision support system for determining tourist purpose using Fuzzy logic with clustering method is an effective solution to help prospective tourists find dream vacation at an affordable price. This system provides comprehensive information and helps prospective tourists make appropriate decisions based on their needs and preferences.

Future Research Directions

Future research directions for this study include:

Improving the clustering method: The FCM clustering method can be improved to provide more accurate results.
Increasing the number of tourist destinations: The system can be expanded to include more tourist destinations.
Developing a mobile application: A mobile application can be developed to provide tourists with easy access to the system.

References

[1] K. Zadeh, "Fuzzy sets," Information and Control, vol. 8, no. 3, pp. 338-353, 1965.
[2] J. C. Bezdek, "Pattern recognition with fuzzy objective function algorithms," Plenum Press, 1981.
[3] M. A. El-Sayed, "Fuzzy clustering algorithms for image segmentation," Pattern Recognition Letters, vol. 22, no. 1, pp. 1-11, 2001.

Appendices

Appendix A: System Requirements
- Operating System: Windows 10
- Programming Language: Visual Basic 6.0
- Database: MySQL
Appendix B: System Design Diagram
- The system design diagram is shown in Figure 1.
- Figure 1: System Design Diagram

Note: The references and appendices are not included in the original content, but are added here to provide a complete article.
Decision Supporting System for Determination of Tourist Purpose Using Fuzzy Logic by Clustering Method: Q&A

Introduction

The decision supporting system for determining tourist purpose using Fuzzy logic with clustering method is a powerful tool for helping prospective tourists find dream vacation at an affordable price. However, there may be some questions and concerns about how the system works and how it can be used. In this article, we will answer some of the most frequently asked questions about the system.

Q: What is the purpose of the decision supporting system?

A: The purpose of the decision supporting system is to help prospective tourists find tourist destinations that meet their needs and budget. The system uses Fuzzy logic and clustering method to suggest various cost factors and provide comprehensive information about various choices of tourist destinations.

Q: How does the system work?

A: The system works by collecting data on various cost factors, including hotel, restaurant, and tourist activities. The system then uses the FCM clustering method to group the data into three groups based on the cost level. The system adjusts the grouped data to tourist preferences, such as the length of vacation time and the available budget. Finally, the system suggests various cost factors using a more flexible Fuzzy method and in accordance with tourist preferences.

Q: What are the advantages of the decision supporting system?

A: The decision supporting system has several advantages, including:

Helping tourists find tourist destinations in accordance with the budget: The system helps prospective tourists find tourist destinations that are in accordance with their budget by combining various cost factors.
Facilitating the vacation planning process: The system can help prospective tourists choose tourist destinations that suit their needs and preferences easily and quickly.
Providing comprehensive information: The system provides information about various choices of tourist destinations, including hotel, restaurant, and tourist activities.

Q: What are the limitations of the decision supporting system?

A: The decision supporting system has several limitations, including:

Limited data: The system is only as good as the data it is based on. If the data is incomplete or inaccurate, the system may not provide the best results.
Limited clustering method: The FCM clustering method used by the system may not be the best method for all types of data.
Limited tourist preferences: The system only takes into account a limited number of tourist preferences, such as the length of vacation time and the available budget.

Q: How can I use the decision supporting system?

A: To use the decision supporting system, you will need to:

Collect data: Collect data on various cost factors, including hotel, restaurant, and tourist activities.
Input data: Input the data into the system.
Run the system: Run the system to get the results.
Analyze results: Analyze the results to determine the best tourist destination for your needs and budget.

Q: What are the future research directions for the decision supporting system?

A: The future research directions for the decision supporting system include:

Improving the clustering method: The FCM clustering method can be improved to provide more accurate results.
Increasing the number of tourist destinations: The system can be expanded to include more tourist destinations.
Developing a mobile application: A mobile application can be developed to provide tourists with easy access to the system.

Q: What are the potential applications of the decision supporting system?

A: The decision supporting system has a wide range of potential applications, including:

Tourism industry: The system can be used by tour operators, travel agencies, and other tourism industry professionals to help tourists find the best tourist destinations.
Travel planning: The system can be used by tourists to plan their trips and find the best tourist destinations.
Research: The system can be used by researchers to study the behavior of tourists and the impact of tourism on local communities.

Conclusion

The decision supporting system for determining tourist purpose using Fuzzy logic with clustering method is a powerful tool for helping prospective tourists find dream vacation at an affordable price. The system has several advantages, including helping tourists find tourist destinations in accordance with the budget, facilitating the vacation planning process, and providing comprehensive information. However, the system also has several limitations, including limited data, limited clustering method, and limited tourist preferences. The future research directions for the system include improving the clustering method, increasing the number of tourist destinations, and developing a mobile application. The potential applications of the system include the tourism industry, travel planning, and research.