Application for Recommending Tourist Attractions on The Island of Java with Content Based Filtering Using Cosine Similarity

Mutiara Sovina; Yusfrizal Yusfrizal; Faisal Amir Harahap; Ivi Lazuly

doi:10.59934/jaiea.v3i2.429

Authors

Mutiara Sovina Universitas Potensi Utama
Yusfrizal Yusfrizal Universitas Potensi Utama
Faisal Amir Harahap Universitas Potensi Utama
Ivi Lazuly Universitas Potensi Utama

DOI:

https://doi.org/10.59934/jaiea.v3i2.429

Keywords:

Tourism; Tourist Attraction; Content Based Filtering; Cosine Similarity

Abstract

Indonesia is a country with a high level of tourism, with natural beauty and historical places and other tourist destinations that continue to develop from year to year. Java Island is one of the islands visited by many tourists with various tourist attractions. Currently, many tourists like to travel, but during holidays tourists are confused about which tourist destination to visit. With advances in internet technology and the abundance of information in online media, it can make it easier for tourists to find information, but because there is so much information provided, it will make tourists confused about deciding and choosing a place. A tourist recommendation application is very necessary to provide good recommendation accuracy to make it easier for tourists to find tourist destinations according to the desired category. To get the best results, the Content Based Filtering method using Cosine Similarity used in this research will provide several tourist recommendations according to the level of similarity of various cities on the island of Java.

Downloads

Download data is not yet available.

References

R. Dodds, A. Ali, and K. Galaski, “Mobilizing knowledge: Determining key elements for success and pitfalls in developing community-based tourism,” Curr. Issues Tour., vol. 21, no. 13, pp. 1547–1568, 2018.

S. L. Kolasinski et al., “2019 American College of Rheumatology/Arthritis Foundation guideline for the management of osteoarthritis of the hand, hip, and knee,” Arthritis Rheumatol., vol. 72, no. 2, pp. 220–233, 2020.

E. Bolturk and C. Kahraman, “A novel interval-valued neutrosophic AHP with cosine similarity measure,” Soft Comput., vol. 22, pp. 4941–4958, 2018.

D. Gunawan, C. A. Sembiring, and M. A. Budiman, “The implementation of cosine similarity to calculate text relevance between two documents,” in Journal of physics: conference series, 2018, vol. 978, p. 12120.

M. Nilashi, O. Ibrahim, and K. Bagherifard, “A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques,” Expert Syst. Appl., vol. 92, pp. 507–520, 2018.

G. Geetha, M. Safa, C. Fancy, and D. Saranya, “A hybrid approach using collaborative filtering and content based filtering for recommender system,” in Journal of Physics: Conference Series, 2018, vol. 1000, p. 12101.

S. H. Nallamala, U. R. Bajjuri, S. Anandarao, D. D. Prasad, and P. Mishra, “A Brief Analysis of Collaborative and Content Based Filtering Algorithms used in Recommender Systems,” in IOP Conference Series: Materials Science and Engineering, 2020, vol. 981, no. 2, p. 22008.

D. Liu, X. Chen, and D. Peng, “Some cosine similarity measures and distance measures between q‐rung orthopair fuzzy sets,” Int. J. Intell. Syst., vol. 34, no. 7, pp. 1572–1587, 2019.

R. Glauber and A. Loula, “Collaborative filtering vs. content-based filtering: differences and similarities,” arXiv Prepr. arXiv1912.08932, 2019.

K. Ding, K. Ma, S. Wang, and E. P. Simoncelli, “Image quality assessment: Unifying structure and texture similarity,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 5, pp. 2567–2581, 2020.

L. Fu and X. Ma, “An improved recommendation method based on content filtering and collaborative filtering,” Complexity, vol. 2021, pp. 1–11, 2021.

J. Huetle-Figueroa, F. Perez-Tellez, and D. Pinto, “Measuring semantic similarity of documents with weighted cosine and fuzzy logic,” J. Intell. Fuzzy Syst., vol. 39, no. 2, pp. 2263–2278, 2020.

I. Indriyanto and I. D. Sumitra, “Measuring the level of plagiarism of thesis using vector space model and cosine similarity methods,” in IOP Conference Series: Materials Science and Engineering, 2019, vol. 662, no. 2, p. 22111.

S. Sintia, S. Defit, and G. W. Nurcahyo, “Product Codefication Accuracy With Cosine Similarity And Weighted Term Frequency And Inverse Document Frequency (TF-IDF),” J. Appl. Eng. Technol. Sci., vol. 2, no. 2, pp. 62–69, 2021.