SISRES: Web-Based Culinary Recommendation with Collaborative Filtering

Qurrotul Aini, Fadly Hakim Muhammad, Eri Rustamaji, Yamin Thwe

Abstract


Due to its advantageous location on the border of Jakarta and Tangerang, Tangerang Selatan is a highly developed city. In this instance, the Tangerang Selatan Department of Culture and Tourism (Dispar Tangsel) is required to use the application to assist the general public in realizing the availability of information in the Tangerang Selatan area. Twenty to twenty-five restaurants submit applications each year to the Dispar Tangsel Tourism Business Registry (TDUP). Dispar Tangsel must choose and decide on TDUP licensing priorities from among TDUP requests in order to open a restaurant. The purpose of the research is to offer recommendations to the community on food choices. Rapid application development (RAD) was used in the system's development. Additionally, the collaborative filtering technique has been employed by the decision support system to determine the amount of criteria or weight for restaurants using the weighted sum algorithm and for restaurants using cosine-based similarity algorithms. Additionally, the system design tool made use of MySQL as a database, PHP, the Codeigniter framework, and the unified modeling language (UML). The result demonstrates that the system is capable of displaying the output in accordance with the user's expectations during black box testing, which evaluates the functionality of the system based on the specifications. Collaborative filtering in SISRES can yield a significant improvement in recommendation accuracy. By collectively analyzing user preferences and behaviors, the algorithm can provide more relevant and personalized recommendations.


Keywords


Collaborative filtering, pearson correlation similarity, culinary, MySQL, rapid application development

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References


BPS Kota Tangerang Selatan, “Statistik Daerah Kota Tangerang Selatan 2018,” Tangselkota.bps.go.id, Sep. 26, 2018. https://tangselkota.bps.go.id/publication/2018/09/26/02a6aea2a2e0f13bbab2731b/statistik-daerah-kota-tangerang-selatan-2018.html (accessed Jul. 14, 2020).

N. F. Al-Bakri and S. H. Hashim, “A Study on the accuracy of prediction in recommendation system based on similarity measures,” Baghdad Sci. J., vol. 16, no. 1 Suppl, pp. 263–269, 2019, doi: 10.21123/bsj.2019.16.1(Suppl.).0263

R. A. Nadhifah, Y. M. Arif, H. Nurhayati, and L. S. Angreani, “Performance of multi-criteria recommender system using cosine-based similarity for selecting halal tourism,” Appl. Inf. Syst. Manage., vol. 5, no. 2, pp. 111–116, Oct. 2022, doi: 10.15408/aism.v5i2.25035

MovieLens, “MovieLens 100K Dataset,” Grouplens.org.

E. Erlangga and H. Sutrisno, “Sistem Rekomendasi Beauty Shop Berbasis Collaborative Filtering,” EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi, vol. 10, no. 2, pp. 47–52, 2020, doi: 10.36448/jmsit.v10i2.1611.

G. Xu, Z. Tang, C. Ma, Y. Liu, and M. Daneshmand, “A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context,” Journal of Electrical and Computer Engineering, vol. 2019, pp. 1–12, Jun. 2019, doi: 10.1155/2019/7070487.

A. F. Rifai and E. B. Setiawan, “Memory-based Collaborative Filtering on Twitter Using Support Vector Machine Classification,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 5, pp. 702–709, 2022, doi: 10.29207/resti.v6i5.4270.

K. Kendall and J. E. Kendall, Systems Analysis and Design, 8th ed. Englewood Cliffs, USA: Prentice Hall, 2011.

A. Glaschenko, “What is rapid application development,” Jmix.io. https://www.jmix.io/rapid-application-development/ (accessed Dec. 16, 2022).

B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, “Item-based collaborative filtering recommendation algorithms,” in Tenth International Conference on World Wide Web (WWW ’01), 2001, pp. 285–295. doi: 10.1145/371920.372071.

T. Ahmad, J. Iqbal, A. Ashraf, D. Truscan, and I. Porres, “Model-based testing using UML activity diagrams: A systematic mapping study,” Computer Science Review, vol. 33, pp. 98–112, 2019, doi: 10.1016/j.cosrev.2019.07.001.

O. Viandari and Q. Aini, “Design and Analysis of Management Information System Qurban (SIMAQ) at PKPU Human Initiative, East Jakarta,” Appl. Inf. Syst. Manage. (AISM), vol. 3, no. 2, pp. 79–86, Oct. 2020, doi: 10.15408/aism.v3i2.11025.

M. S. Mahfudz, Z. Arham, and E. Khudzaeva, “Development of Web-based Spatial Information System Tourism Industry Event Distribution (Case Study: DKI Jakarta),” Appl. Inf. Syst. Manage. (AISM), vol. 3, no. 2, pp. 107–112, Oct. 2020, doi: 10.15408/aism.v3i2.17986.

S. Rizky, Konsep Dasar Rekayasa Perangkat Lunak. Jakarta: Prestasi Pustaka Publisher, 2011.

F. Zhang, T. Gong, V. E. Lee, G. Zhao, C. Rong, and G. Qu, “Fast algorithms to evaluate collaborative filtering recommender systems,” Knowledge-Based Systems, vol. 96, pp. 96–103, 2016, doi: 10.1016/j.knosys.2015.12.025.

J. Bobadilla, F. Ortega, A. Hernando, and A. Gutierrez, “Recommender systems survey,” Knowledge-Based Systems, vol. 46, no. 1, pp. 109–132, 2013, doi: 10.1016/j.knosys.2013.03.012.

J. S. Breese, D. Heckerman, and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering,” Online UAI-P-1998-PG-43-52, 2013. [Online]. Available: https://arxiv.org/abs/1301.7363

J. B. Schafer, D. Frankowski, J. Herlocker, and S. Sen, “Collaborative Filtering Recommender Systems,” in The Adaptive Web, P. Brusilovsky, A. Kobsa, W. Nejdl (eds).in Lecture Notes in Computer Science, vol. 4321. Heidelberg, Berlin, Germany: Springer, 2007, pp. 291–324.

K. Abhishek, S. Kulkarni, V. K. N., and A. P. Kumar, “A Review on Personalized Information Recommendation System Using Collaborative Filtering,” International Journal of Computer Science and Information Technologies, vol. 2, no. 3, pp. 1272–1278, 2011.

A. A. Fakhri, Z. K. A. Baizal, and E. B. Setiawan, “Restaurant Recommender System Using User-Based Collaborative Filtering Approach: A Case Study at Bandung Raya Region,” J. Phys.: Conf. Ser., vol. 1192, p. 012023, Mar. 2019, doi: 10.1088/1742-6596/1192/1/012023.

N. Rajabpour, A. Mohammadighavam, A. Naserasadi, and M. Estilayee, “TFR: A Tourist Food Recommender System based on Collaborative Filtering,” IJCA, vol. 181, no. 11, pp. 30–39, Aug. 2018, doi: 10.5120/ijca2018917695.

A. Yusmar, L. K. Wardhani, and H. B. Suseno, “Restaurant Recommender System Using Item based Collaborative Filtering and Adjusted Cosine Algorithm Similarity,” j. Teknik inform., vol. 14, no. 1, pp. 93–100, Sep. 2021, doi: 10.15408/jti.v14i1.21102.




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