AHP–SAW-Based Decision Support System for Culinary Tourism Restaurant Selection in Semarang City - Cahaya, Agus Indra; Aprico, Fikky; Apriyanti, Dewi; Nugroho, Kristiawan; Ardhianto, Eka (Information Technology and Science (ITScience), 2025)
Semarang City boasts a diverse array of appealing culinary restaurants, yet both tourists and local residents frequently encounter difficulties in selecting dining establishments that best match their preferences, often diminishing their culinary tourism experience and leading to inefficient time usage. This research aims to develop a decision support model by implementing a combined approach of the Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW) to streamline the restaurant selection process. AHP was selected for its capability to establish hierarchical criteria weights, while SAW offers an efficient method for ranking alternatives. The initial phase utilized AHP to determine the weights of six identified relevant primary criteria: Cleanliness, Location, Price, Ambiance, Taste, and Rating. Assessments for each of these criteria were gathered through surveys administered to 30 culinary enthusiasts in Semarang City. Subsequently, the second phase employed SAW to calculate the final scores for 10 culinary tourism restaurants in Semarang, evaluated through questionnaires by 10 different respondents. The calculation results placed Folkcafe at ALVA in the first rank (with a score of 0.8656), followed by Ikan Bakar Cianjur in the second rank (score of 0.8524), and Pelangi Cheese Chiffon Cake in the third rank (score of 0.8173). These findings unequivocally demonstrate the effectiveness of applying AHP and SAW for prioritizing culinary restaurants in Semarang, further supported by the valid consistency of the AHP criteria weights (CR = 0.0341). This study contributes to the DSS literature by combining AHP and SAW in the underexplored context of culinary tourism ranking. This model is expected to serve as a practical guide for visitors and a foundational basis for the development of digital recommendation systems within the culinary tourism sector.