Comparison of MOORA and WASPAS in the Banyuwangi Nature Tourism Selection DSS

Dyapradita Eka  Febiasterina; I Gusti Ayu Agung Diatri Indradewi; Gede Surya Mahendra

doi:10.31004/riggs.v5i1.6063

Authors

Dyapradita Eka Febiasterina Universitas Pendidikan Ganesha
I Gusti Ayu Agung Diatri Indradewi Universitas Pendidikan Ganesha
Gede Surya Mahendra Universitas Pendidikan Ganesha

DOI:

https://doi.org/10.31004/riggs.v5i1.6063

Keywords:

DSS, MOORA, WASPAS, Tourism, Banyuwangi

Abstract

This study compares two multi-criteria Decision Support System (DSS) methods, Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) and Weighted Aggregated Sum Product Assessment (WASPAS), for ranking natural tourism destinations in Banyuwangi Regency, Indonesia. Using a quantitative design, survey data were collected from 50 respondents who assessed 48 destinations using five criteria like facilities, entrance fee, safety, travel distance, and cleanliness. The analysis followed the CRISP-DM framework from business understanding through evaluation and interpretation. The MOORA method applied vector normalization and benefit cost optimization, while the WASPAS method combined weighted sum and weighted product models to produce preference scores. Results show that Bangsring Underwater emerged as the most competitive destination overall, achieving preference values of 0.1932 using MOORA and 0.6837 using WASPAS for Decision Maker 1. Sensitivity testing across ten weight variation scenarios indicated that WASPAS showed stronger individual level dominance, ranking the top alternative first in 8 of 10 scenarios, while MOORA ranked first in 7 of 10 scenarios. However, when extended to all respondents, MOORA demonstrated higher population level robustness and slightly higher average accuracy at 51.61% than WASPAS at 50.32%. These findings indicate a trade-off between stability and responsiveness. MOORA is preferable for generalized tourism planning involving diverse stakeholders, while WASPAS is better suited for adaptive or personalized recommendation contexts.

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