Penerapan Algoritma Dijkstra Dan Metode Topsis Dalam Sistem Rekomendasi Barbershop Berbasis Android

Adil Priman Hati Hulu; Anzas Ibezato Zalukhu

Authors

Adil Priman Hati Hulu STMIK Methodist Binjai
Anzas Ibezato Zalukhu STMIK Methodist Binjai

Keywords:

Dijkstra, TOPSIS, Recommendation System, Barbershop, Android

Abstract

The rapid growth of modern barbershops in Medan City has increased user difficulties in determining the nearest location while selecting the best barbershop based on multiple service criteria. The main problems involve finding optimal routes and selecting the best alternatives based on price, service quality, style, health protocols, and distance. This study develops an Android-based barbershop recommendation system by integrating the Dijkstra algorithm for shortest path search and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method for alternative ranking. Dijkstra calculates the shortest distance from the user's location, while TOPSIS determines the best recommendation using weighted criteria. The results show that the system effectively provides the fastest routes and optimal recommendations according to user preferences, improving search efficiency and decision-making accuracy.

References

Z. Zheng, Y. Yang, and H. Wang, “A survey of location-based recommender systems,” ACM Computing Surveys, 2020.

A. Vitianingsih et al., “Recommendation system for supplier selection: Comparison of Profile Matching and TOPSIS,” INTENSIF, 2024.

H. Tong, “Graph-based methods for recommender systems,” Foundations and Trends in Information Retrieval, 2018.

H. Siang, Matematika Diskrit dan Aplikasinya. Andi, 2009.

L. Rokach and O. Maimon, Data Mining with Decision Trees: Theory and Applications. World Scientific, 2014.

S. Prayoga, “Implementasi perencanaan jalur menggunakan algoritma Dijkstra,” Jurnal Automasi dan Elektronika, 2024.

R. Parmanand, P. Sharma, and D. Mehta, “Comparative study of Dijkstra and Greedy algorithms for shortest path in GIS,” in Proceedings of the International Conference on Smart Systems and Advanced Computing, 2024.

S. Ojagh, F. Ricci, and C. Hurter, “A location-based orientation-aware recommender system,” Expert Systems with Applications, vol. 139, p. 112859, 2020.

M. A. H. Nugraha and D. Suprianto, “Pengembangan aplikasi Android dengan Google Maps API untuk sistem berbasis lokasi,” Jurnal Teknologi dan Sistem Komputer, 2019.

D. Novriansyah, “TOPSIS: teori dan aplikasi,” Jurnal Teknologi dan Sistem Komputer, 2014.

P. Longley, M. F. Goodchild, D. Maguire, and D. Rhind, Geographic Information Systems and Science, 3rd ed. Wiley, 2015.

A. Lakshmi, “A hybrid recommendation model for tourist route optimization,” SN Computer Science, 2024.

T. A. Labibah, “Pencarian rute trayek angkot terpendek menggunakan algoritma Dijkstra dan Haversine formula,” Jurnal Teknologi, 2018.

M. H. K. Kiani, M. R. Tayarani-Najarn, and S. M. H. Hashemi, “An enhanced TOPSIS method with entropy weights for decision making,” International Journal of Decision Support Systems, 2021.

M. T. Huda, “Recommendation system for mobile applications tourist guide,” Journal of Web Systems, 2023.

M. Ekstrand, J. Konstan, and J. Riedl, Collaborative Filtering Recommender Systems. Now Foundations, 2011.

M. D. Ekhlakov et al., “Hybrid MCDM systems: AHP weighting and TOPSIS ranking for route priority,” Mathematics, 2024.

R. Ekhlakov, A. Vikhrov, and A. Sokolov, “Multicriteria assessment method for network structure design using hybrid AHP–TOPSIS,” Mathematics, vol. 12, no. 4, 2024.

R. Burke, “Hybrid recommender systems: Survey and experiments,” User Modeling and User-Adapted Interaction, vol. 12, no. 4, pp. 331–370, 2002.

G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734–749, 2005.