Group Decision Support System based on AHP-TOPSIS for Culinary Recommendation System

This paper proposes the integration of AHP and TOPSIS to generate the ranking results of culinary recommendation for a group of users to provide better recommendation results. Formerly, Group Decision Support System (GDSS) for culinary recommendations has been developed with the TOPSIS method. TOPSIS has low algorithm complexity, so it is suitable to be applied in mobile devices. However, GDSS with TOPSIS has its disadvantages, TOPSIS have not been able to facilitate the preferences of each user inside a group so the recommendation result always consist only on dominant user. TOPSIS method produces unchanging rankings, because this method recommends a food menu based on the 1 dominant user so that the ranking is always consistent. Meanwhile, this study aims to integrate AHP for weighting criteria from each user and TOPSIS for ranking culinary recommendations. Based on rank consistency testing results that conducted in 6 different user groups, unlike the previous research, AHP-TOPSIS shows inconsistency ranking, which means that changes in user preferences affect the recommendation results that are generated by application. The AHP-TOPSIS method proved can be accommodated the computation of various preferences of each user in GDSS culinary recommendatio