Combination of LOPCOW and MOORA in Restaurant Recommendation Decision Support System Based on User Reviews

Setiawansyah Setiawansyah

doi:10.58602/itsecs.v2i3.153

Setiawansyah Setiawansyah Universitas Teknokrat Indonesia

DOI: https://doi.org/10.58602/itsecs.v2i3.153

Keywords: Combination, Decision Support System, LOPCOW, MOORA, Recommendations

Abstract

A restaurant is a place of business that provides various types of food and beverages to be consumed on the spot or taken home. Restaurants are also often the venue of choice for various events, such as family gatherings, birthday celebrations, or business meetings. Restaurant recommendations based on user reviews are becoming an increasingly popular approach in helping individuals find the best places to eat according to their preferences. The purpose of the research is to develop an effective and objective decision support system in providing the best restaurant recommendations based on user reviews. This research makes a significant contribution to improving the quality of the user review-based recommendation system, by combining the advantages of objective data analysis (LOPCOW) and multi-criteria optimization (MOORA). The results of the combined ranking of the LOPCOW and MOORA methods show that Kedai Kita is ranked the highest with a score of 0.2897, followed by De'leuit Restaurant with a score of 0.284. Lemongrass Restaurant is in third place with a score of 0.2485, while Kluwih Sunda Authentic and Gurih 7 Bogor obtained a score of 0.2223 and 0.1981, respectively. The last position was occupied by RM Bumi Aki Puncak with the lowest score, which was 0.0396. These results show differences in performance or quality levels based on criteria analyzed using a combination of the two methods.

References

J. Wang, A. R. Isnain, R. R. Suryono, Y. Rahmanto, M. Mesran, and S. Setiawansyah, “Decision Support System for Platform Selection in E-Commerce Using the OWH-TOPSIS Method,” J. Comput. Syst. Informatics, vol. 6, no. 1, pp. 172–181, 2024, doi: 10.47065/josyc.v6i1.5990.

J. Wang, S. Setiawansyah, and Y. Rahmanto, “Decision Support System for Choosing the Best Shipping Service for E-Commerce Using the SAW and CRITIC Methods,” J. Ilm. Inform. dan Ilmu Komput., vol. 3, no. 2, pp. 101–109, 2024, doi: 10.58602/jima-ilkom.v3i2.32.

J. Wang, D. Darwis, S. Setiawansyah, and Y. Rahmanto, “Implementation of MABAC Method and Entropy Weighting in Determining the Best E-Commerce Platform for Online Business,” JiTEKH, vol. 12, no. 2, pp. 58–68, 2024, doi: 10.35447/jitekh.v12i2.1000.

J. H. Lubis, M. Mesran, and C. A. Siregar, “The Decision Support System for Cashier Recruitment Implements the Multi-Attribute Utility Theory Method,” Build. Informatics, Technol. Sci., vol. 6, no. 1, pp. 257–264, 2024.

Andris Silitonga and Dyah Ayu Megawaty, “Decision Support System Feasibility for Promotion using the Profile Matching Method,” J. Data Sci. Inf. Syst., vol. 1, no. 2 SE-Articles, pp. 50–56, May 2023, doi: 10.58602/dimis.v1i2.46.

J. Tupayachi, H. Xu, O. A. Omitaomu, M. C. Camur, A. Sharmin, and X. Li, “Towards Next-Generation Urban Decision Support Systems through AI-Powered Construction of Scientific Ontology Using Large Language Models—A Case in Optimizing Intermodal Freight Transportation,” Smart Cities, vol. 7, no. 5. pp. 2392–2421, 2024. doi: 10.3390/smartcities7050094.

J. Barman, B. Biswas, and K. S. Rao, “A hybrid integration of analytical hierarchy process (AHP) and the multiobjective optimization on the basis of ratio analysis (MOORA) for landslide susceptibility zonation of Aizawl, India,” Nat. Hazards, vol. 120, no. 9, pp. 8571–8596, 2024, doi: 10.1007/s11069-024-06538-9.

T. Al Mindeel, E. Spentzou, and M. Eftekhari, “Energy, thermal comfort, and indoor air quality: Multi-objective optimization review,” Renew. Sustain. Energy Rev., vol. 202, p. 114682, 2024, doi: https://doi.org/10.1016/j.rser.2024.114682.

J. Fu, H. Wang, X. Sun, H. Bao, X. Wang, and J. Liu, “Multi-objective optimization for impeller structure parameters of fuel cell air compressor using linear-based boosting model and reference vector guided evolutionary algorithm,” Appl. Energy, vol. 363, p. 123057, 2024, doi: https://doi.org/10.1016/j.apenergy.2024.123057.

M. D. S. Rahardjo, “Sistem Pendukung Keputusan Pemilihan Guru Berprestasi Menerapkan Metode AHP dan MOORA,” Resolusi Rekayasa Tek. Inform. dan Inf., vol. 4, no. 5, pp. 467–475, 2024, doi: 10.30865/resolusi.v4i5.1896.

Y. Alkhalifi, M. R. Firdaus, D. Ismunandar, and I. Herliawan, “Analisis Perbandingan Metode SMART Dan MOORA Pada Pemilihan Karyawan Terbaik Klinik Kecantikan,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 4, no. 4, pp. 1972–1982, 2024, doi: 10.30865/klik.v4i4.1620.

Ö. Işık, M. Shabir, and S. Moslem, “A hybrid MCDM framework for assessing urban competitiveness: A case study of European cities,” Socioecon. Plann. Sci., vol. 96, p. 102109, 2024, doi: https://doi.org/10.1016/j.seps.2024.102109.

Z. Guo et al., “An integrated MCDM model with enhanced decision support in transport safety using machine learning optimization,” Knowledge-Based Syst., vol. 301, p. 112286, 2024, doi: https://doi.org/10.1016/j.knosys.2024.112286.

A. A. Izka and H. Sulistiani, “Penerapan Metode Pembobotan LOPCOW dan Grey Relational Analysis Dalam Penentuan Pemasok Toserba Terbaik,” J. Inf. Syst. Res., vol. 5, no. 4, pp. 1352–1360, 2024, doi: 10.47065/josh.v5i4.5537.

V. Simic, S. Dabic-Miletic, E. B. Tirkolaee, Ž. Stević, A. Ala, and A. Amirteimoori, “Neutrosophic LOPCOW-ARAS model for prioritizing industry 4.0-based material handling technologies in smart and sustainable warehouse management systems,” Appl. Soft Comput., vol. 143, p. 110400, Aug. 2023, doi: 10.1016/j.asoc.2023.110400.