Main Article Content


The utilization of the internet in the tourism is very helpful for tourists in planning trips, including in exploring local tourist areas. The existence of special media such as smart tourism web facilities to publish local tourist attractions and tourist facilities in the local area certainly help stakeholders and tourists. To facilitate the tourists as web users to access information about visited area, it is necessary to have a recommendation system that can provide a recommendation regarding the tourist needs such as tourist destinations, lodging, restaurants and even souvenir shops typical of the local area. Recommendation system development can be done through two basic methods, namely: Content Based Recommendation and Collaborative Filtering. This study aims to show how to implement content-based filtering in providing content-based recommendations on supporting the development of smart tourism webby utilizing cosine similarity and K-Nearest Neighbor. This study shows that Content Based Recommendation can provide recommendation according to the tourist needs based on the content that has been selected by other users.


Sistem Rekomendasi; Content Based Recommendation; Web Smart Tourism Recommendation System Content Based Recommendation Smart Tourism Web

Article Details

Author Biographies

Nuralamsah Zulkarnaim, Universitas Sulawesi Barat

Teknik Informatika Universitas Sulawesi Barat

Musyrifah, Universitas Sulawesi Barat

Teknik Informatika Universitas Sulawesi Barat

Sulfayanti, Universitas Sulawesi Barat

Teknik Informatika Universitas Sulawesi Barat

Irfan , Universitas Sulawesi Barat

Teknik Informatika Universitas Sulawesi Barat

Asmawati, Universitas Sulawesi Barat

Teknik Informatika Universitas Sulawesi Barat
How to Cite
Zulkarnaim, N., Musyrifah, Sulfayanti, Ap, I. ., & Asmawati. (2022). Sistem Rekomendasi Berbasis-Konten Untuk Pengembangan Web Smart Tourism. Jurnal Komputer Terapan , 8(1), 143–150.


  1. N. Prasetio, “Recommendation System Dengan Python: Definisi (Part 1),” Data Folks Indonesia, Jul. 26, 2019. (accessed Oct. 10, 2021).
  2. J. Borràs, A. Moreno, and A. Valls, “Intelligent tourism recommender systems: A survey,” Expert Systems with Applications, vol. 41, no. 16, pp. 7370–7389, Nov. 2014, doi: 10.1016/j.eswa.2014.06.007.
  3. R. Hassannia, A. Vatankhah Barenji, Z. Li, and H. Alipour, “Web-Based Recommendation System for Smart Tourism: Multiagent Technology,” Sustainability, vol. 11, no. 2, Art. no. 2, Jan. 2019, doi: 10.3390/su11020323.
  4. G. Nemade, R. Deshmane, P. Thakare, M. Patil, and V. D. Thombre, “SMART TOURISM RECOMMENDER SYSTEM,” vol. 05, no. 06, p. 3, 2018.
  5. F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds., Recommender Systems Handbook. Boston, MA: Springer US, 2011. doi: 10.1007/978-0-387-85820-3.
  6. B. Rocca, “Introduction to recommender systems,” Medium, Jun. 12, 2019. (accessed Oct. 13, 2021).
  7. H. H. Hlaing and K. T. Ko, “Location-Based Recommender System for Mobile Devices on University Campus,” presented at the International Conference on Future Computational Technology (ICFCT 2015), Singapore, Mar. 2015. doi:
  8. M. Batet, A. Moreno, D. Sánchez, D. Isern, and A. Valls, “Turist@: Agent-based personalised recommendation of tourist activities,” Expert Systems with Applications, vol. 39, pp. 7319–7329, Mar. 2012, doi: 10.1016/j.eswa.2012.01.086.
  9. B. S. Fitrianti, M. Fachurrozi, and N. Yusliani, “Sistem Rekomendasi Artikel Ilmiah Berbasis Web Menggunakan Content-based Learning dan Collaborative Filtering,” Generic, vol. 10, no. 1, Art. no. 1, Jan. 2018.
  10. O. Sutton, “Introduction to k Nearest Neighbour Classification and Condensed Nearest Neighbour Data Reduction,” p. 10.