Sistem Pendukung Keputusan Hotel Bintang Empat Berbasis SAW dan Analisis Sentimen Daring

Abstract

Selecting a four-star hotel in Riau Province is a multi-criteria decision-making problem because travelers must simultaneously consider price, user ratings, facilities, location, service quality, and customer perceptions. This study aims to develop a decision support model based on the Simple Additive Weighting (SAW) method by integrating online review sentiment to generate structured and transparent hotel recommendations. A quantitative research approach was employed through problem identification, collection of alternative and criterion data, sentiment processing, decision matrix construction, normalization, weighting, and alternative ranking. Data analysis classified room price as a cost attribute, while user rating, facilities, location, service quality, and sentiment were treated as benefit attributes. The value of each alternative was normalized according to its attribute type, multiplied by the corresponding criterion weight, and aggregated to obtain the final preference score. An illustrative evaluation of five alternatives showed that A2 achieved the highest preference score of 0.9418, followed by A1 at 0.9293, A4 at 0.9240, A5 at 0.9194, and A3 at 0.9126. These findings indicate that the lowest-priced hotel does not necessarily become the best alternative because the final recommendation depends on the balanced performance of all criteria. The novelty of this study lies in incorporating online review sentiment scores into the SAW model, thereby extending conventional hotel evaluation that is commonly dominated by numerical data and aggregate ratings. Theoretically, this study strengthens the application of multi-criteria decision-making in the accommodation sector. Practically, the proposed model can assist travelers in comparing hotels more objectively and help hotel managers identify service attributes that influence their competitive position. Nevertheless, further empirical implementation requires actual hotel data, sentiment classification validation, weight sensitivity analysis, and comparisons with other decision-making methods to evaluate ranking robustness.