Optimasi Rekomendasi Destinasi Wisata Menggunakan Metode CBF Dan GIS Dengan Pendekatan Iterative
DOI:
https://doi.org/10.61805/fahma.v24i3.249Keywords:
Content Based Filtering, Geographic Information System, Iterative Development, Sistem Rekomendasi Destinasi Wisata, Sistem InformasiAbstract
The Jakarta Provincial Tourism and Creative Economy Office plays a key role in managing and developing tourist destinations, including providing information through Tourist Information Centers (TICs). However, the existing manual recommendation process does not adequately accommodate tourist preferences, while visitor feedback is not centrally documented. This study aims to develop a web-based tourist destination recommendation system that provides preference-based recommendations, geographic information, and feedback features to support destination evaluation. Content-Based Filtering (CBF) is applied by matching user preferences with destination characteristics using semantic embeddings and cosine similarity. A Geographic Information System (GIS) provides location and route information, while the system is developed using an Iterative Development approach. Recommendation performance was evaluated using Precision and Recall across five query scenarios at K=1, K=3, K=5, and K=10. The results show an average precision of 1.00 at K=1, K=3, and K=5, and 0.86 at K=10. Average recall increased from 0.09 at K=1 to 0.69 at K=10. User evaluation involving 20 respondents achieved an average score of 83.4%. The system assists tourists in finding destinations based on their preferences while providing location, route, and feedback information.
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References
Tourist Recommender System: A Data-Driven and Machine-Learning Approach,” Computation, vol. 12, no. 3, hlm. 59, Mar 2024, doi: 10.3390/computation12030059.
M. S. Viñán-ludeña dan L. De Campos, “Evaluating Tourist Dissatisfaction with Aspect-Based Sentiment Analysis Using Social Media Data,” Advances in Hospitality and Tourism Research (AHTR), vol. 12, no. 3, hlm. 254–286, Sep 2024, doi: 10.30519/ahtr.1436175.
A. Solano-Barliza dkk., “Recommender systems applied to the tourism industry: a literature review,” Cogent Business & Management, vol. 11, no. 1, hlm. 2367088, Des 2024, doi: 10.1080/23311975.2024.2367088.
S. Oyadila, D. Abdullah, dan A. Razi, “IMPLEMENTASI CONTENT-BASED FILTERING DENGAN TF-IDF DAN COSINE SIMILARITY UNTUK SISTEM REKOMENDASI DESTINASI WISATA DI ACEH TENGAH,” rabit, vol. 10, no. 2, hlm. 1329–1339, Jul 2025, doi: 10.36341/rabit.v10i2.6532.
M. N. Arkan, A. P. Widodo, dan G. Aryotejo, “A Web-Based Tourism Recommendation System for Boyolali Using Content-Based Filtering and Cosine Similarity,” JMASIF, vol. 17, no. 1, hlm. 39–53, Jan 2026, doi: 10.14710/jmasif.17.1.72520.
M. Hendre dan P. Mukherjee, “Efficacy of Deep Neural Embeddings- Based Semantic Similarity in Automatic Essay Evaluation,” International Journal of Cognitive Informatics and Natural Intelligence, 2023.
A. Amelia, T. N. Ad, S. Suakanto, dan S. C. R. Rijadi, “Contextual Tourism Access Through Geographic Information System with Recommendation System: The Case of Greater Bandung,” dalam 2025 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), Bali, Indonesia: IEEE, Jul 2025, hlm. 283–289. doi: 10.1109/IAICT65714.2025.11101505.
C. Salsabilla dan D. W. Utomo, “Pekalongan Regency Tourism Recommendation System with Content based Filtering,” SISTEMASI, vol. 14, no. 1, hlm. 262, Jan 2025, doi: 10.32520/stmsi.v14i1.4839.
M. N. Arkan, A. P. Widodo, dan G. Aryotejo, “A Web-Based Tourism Recommendation System for Boyolali Using Content-Based Filtering and Cosine Similarity,” JMASIF, vol. 17, no. 1, hlm. 39–53, Jan 2026, doi: 10.14710/jmasif.17.1.72520.
M. A. Widika, P. S. Susilo, A. I. Ramadhan, S. Nur, F. A. Sariasih, dan I. Sutoyo, “SISTEM REKOMENDASI DESTINASI WISATA BERBASIS CONTENT-BASED FILTERING DAN ANALISIS FITUR GEOSPASIAL,” vol. 11, no. 1, 2026.
S. Huang, “Research on software reliability enhancement methods based on iterative development,” ACE, vol. 76, no. 1, hlm. 21–26, Jul 2024, doi: 10.54254/2755-2721/76/20240558.
K. S. Nugroho, F. A. Bachtiar, dan W. F. Mahmudy, “Detecting Emotion in Indonesian Tweets: A Term-Weighting Scheme Study,” J. Inf. Syst. Eng. Bus. Intell., vol. 8, no. 1, hlm. 61–70, Apr 2022, doi: 10.20473/jisebi.8.1.61-70.
L. Wang dkk., “Text Embeddings by Weakly-Supervised Contrastive Pre-training,” 22 Februari 2024, arXiv: arXiv:2212.03533. doi: 10.48550/arXiv.2212.03533.
L. Wang, N. Yang, X. Huang, L. Yang, R. Majumder, dan F. Wei, “Multilingual E5 Text Embeddings: A Technical Report,” 8 Februari 2024, arXiv: arXiv:2402.05672. doi: 10.48550/arXiv.2402.05672.
H. Hartatik dan A. Syafrianto, “PENERAPAN MODEL SENTENCE-BERT UNTUK SISTEM REKOMENDASI BUKU BERBASIS KONTEN DI PERPUSTAKAAN DIGITAL,” J. Dialektika Inform., vol. 6, no. 1, hlm. 12–19, Nov 2025, doi: 10.24176/detika.v6i1.15916.
T.-D. Nguyen, “An approach to improve the accuracy of rating prediction for recommender systems,” Automatika, vol. 65, no. 1, hlm. 58–72, Jan 2024, doi: 10.1080/00051144.2023.2284026.
M. Acharya dan K. K. Mohbey, “Recency-based spatio-temporal similarity exploration for POI recommendation in location-based social networks,” Front. Sustain. Cities, vol. 6, hlm. 1331642, Apr 2024, doi: 10.3389/frsc.2024.1331642.
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