Mohammad Aldinugroho Abdullah
Universitas Budi Luhur Jakarta, Indonesia

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Pemilihan Waitress Resto Akul Terbaik Menerapkan Metode WASPAS dengan Pembobotan ROC Puspa Ayu Soleha; Rima Tamara Aldisa; Mohammad Aldinugroho Abdullah
Journal of Information System Research (JOSH) Vol 4 No 3 (2023): April 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i3.3375

Abstract

The restaurant is an area that serves culinary delights for public consumption and also has various services to make customers feel at home while eating. One of the factors that determines the success of a restaurant in the food industry is the quality of the waitress service. An employee who serves customers in a restaurant, cafe, dining room or hotel is known as a waitress. These are the people who will approach a client as they walk in to take their order, give them a menu, and be available if they need more. Because it generates data that can be used for administrative decisions such as promotion, training, awards and other assessments, selecting the best restaurant waiter is a very important process in management. The obstacle in choosing the best restaurant waitress is the difficulty for restaurant management to make decisions regarding the best restaurant waitress because of the large amount of data which results in a long processing time. The Weighted Aggregated Sum Product Assessment (WASPAS) approach and the Rank Order Centroid (ROC) weighting method are the two methods used in this study to apply a decision support system to the challenge of selecting the best restaurant waiter. In this study, a number of factors, including appearance, responsibility, discipline, teamwork, high initiative, and multitasking skills, were taken into consideration in selecting the best restaurant servers. The W8 alternative, Ratih Setyowati with a value of 0.9341, which has the right to be chosen as the best restaurant waitress, is the result of a ranking to choose the best restaurant waitress with the highest preference after applying the WASPAS method and ROC weighting.