WEBSITE-BASED RECOMMENDATIONS FOR TOURIST ATTRACTIONS IN BITUNG CITY USING CONTENT-BASED FILTERING ALGORITHMS
Abstract
This study aims to develop a website-based recommendation system for tourist attractions in Bitung City, leveraging content-based filtering algorithms. The primary goal of this research is to provide personalized recommendations to tourists, enhancing their travel experience by suggesting attractions that align with their preferences and interests. The methodology involves the implementation of a content-based filtering algorithm, which analyzes the attributes and features of various tourist spots to generate relevant recommendations. The system evaluates factors such as location, type of attraction, available facilities, and user reviews. The results indicate that the content-based filtering approach effectively identifies and suggests tourist attractions that match the users' interests, thereby improving the overall satisfaction of tourists visiting Bitung City. This recommendation system offers a practical solution for tourists seeking tailored travel experiences and contributes to the promotion of local tourism.
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References
M. Rizq Daffa Jodi, "Fakultas Komputer Algoritma dan Struktur data," Fakultas Komputer, vol. 1, pp. 1–10, 2020.
M. Informatika, P. L. P. I. Bandung, M. Informatika, and P. L. P. I. Bandung, "Zen+M," 01, 2020.
Y. R. Mulyawan and C. C. Lestari, "Rancang Bangun Sistem Rekomendasi Resep Masakan Berdasarkan Bahan Baku Dengan Menggunakan Algoritma Penyaringan Berbasis Konten," JUTI: Jurnal Ilmiah Teknologi Informasi, vol. 17, no. 2, p. 94, 2019, doi: 10.12962/j24068535.v17i2.a791.
N. I. Putri, Y. Rustiyana, Y. Herdiana, and Z. Munawar, "Sistem Rekomendasi Hibrid Pemilihan Mobil Berdasarkan Profil Pengguna dan Profil Barang," Tematik, vol. 8, no. 1, pp. 56–68, 2021, doi: 10.38204/tematik.v8i1.566.
M. R. Saputra, M. Ramadhan, P. Studi, and T. Informatika, "Intisari," n.d.
S. Hashim and J. Waden, "Content-based filtering algorithm in social media," Wasit Journal of Computer and Mathematics Science, vol. 2, no. 1, pp. 14–17, 2023, doi: 10.31185/wjcm.112.
A. Nilla and E. B. Setiawan, "Film Recommendation System Using Content-Based Filtering and the Convolutional Neural Network (CNN) Classification Methods," Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika, vol. 10, no. 1, p. 17, 2024, doi: 10.26555/jiteki.v9i4.28113.
A. E. Putri, "Evaluasi Program Bimbingan Dan Konseling: Sebuah Studi Pustaka," JBKI (Jurnal Bimbingan Konseling Indonesia), vol. 4, no. 2, p. 39, 2019, doi: 10.26737/jbki.v4i2.890.
M. N. Adlini, A. H. Dinda, S. Yulinda, O. Chotimah, and S. J. Merliyana, "Metode Penelitian Kualitatif Studi Pustaka," Edumaspul: Jurnal Pendidikan, vol. 6, no. 1, pp. 974–980, 2022, doi: 10.33487/edumaspul.v6i1.3394.
B. Suprayogi and A. Rahmanesa, "TEMATIK - Jurnal Teknologi Informasi Dan Komunikasi Vol. 6, No. 2 Desember 2019," vol. 6, no. 2, pp. 119–127, 2019.
C. S. D. Prasetya, "Sistem Rekomendasi Pada E-Commerce Menggunakan K-Nearest Neighbor," Jurnal Teknologi Informasi Dan Ilmu Komputer, vol. 4, no. 3, p. 194, 2017, doi: 10.25126/jtiik.201743392.
U. Javed, K. Shaukat, I. A. Hameed, F. Iqbal, T. M. Alam, and S. Luo, "A Review of Content-Based and Context-Based Recommendation Systems," International Journal of Emerging Technologies in Learning, vol. 16, no. 3, pp. 274–306, 2021, doi: 10.3991/ijet.v16i03.18851.
Z. Shahbazi and Y.-C. Byun, "Product Recommendation Based on Content-based Filtering Using XGBoost Classifier," Int. J. Adv. Sci. Technol., vol. 29, no. 04, pp. 6979–6988, 2020.
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