Web-Based Decision Support System for Budget Prediction Using the SAW Method for Tourism Recommendations

Authors

  • Fahmi Zainal Habib Universitas KH. A. Wahab Hasbullah
  • Siti Sufaidah Universitas KH. A. Wahab Hasbullah

DOI:

https://doi.org/10.32764/newton.v3i2.4933

Keywords:

Decision Support System, Simple Additive Weighting, Budget Prediction, Tourism Recommendations,, Web-Based Application

Abstract

Tourism plays a crucial role in regional and national economies, with local tourism in Indonesia, particularly in Jombang Regency, East Java, offering substantial development potential. However, tourists often face difficulties in planning trips that align with their budgets due to inadequate integration of information on costs, trip duration, and participant numbers.

This study develops a web-based Decision Support System (DSS) utilizing the Simple Additive Weighting (SAW) method to enhance trip planning for tourism in Jombang. The DSS integrates data on budget, trip duration, and the number of participants to provide optimal recommendations based on user preferences and constraints. The SAW method facilitates a systematic and objective evaluation process for recommending tourist destinations.

The research follows the ADDIE development model, encompassing Analysis, Design, Development, Implementation, and Evaluation stages. Key objectives include creating a system that consolidates cost, duration, and participant data, applying SAW for accurate recommendations, and ensuring an objective evaluation process. The system aims to improve trip planning efficiency, provide accurate recommendations, and enhance user satisfaction.

The scope of this study is limited to applying the SAW method for budget prediction in tourism recommendations for Jombang. The developed system is a prototype that may require further refinement for comprehensive implementation.

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Published

2024-08-25

How to Cite

Habib, F. Z. ., & Sufaidah, S. (2024). Web-Based Decision Support System for Budget Prediction Using the SAW Method for Tourism Recommendations. NEWTON: Networking and Information Technology, 3(2), 35–42. https://doi.org/10.32764/newton.v3i2.4933

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Section

Articles