Implementasi Sistem Penunjang Keputusan Untuk Destinasi Wisata Di Bogor Menggunakan Metode Collaborative Filtering
Keywords:
Decision Support System, Tourism Recommendation, User-Based Collaborative Filtering, Cosine SimilarityAbstract
Bogor has many tourist destination options with diverse characteristics that often make tourists difficult to determine destinations according to their preferences. Therefore, a system is needed to assist tourists in making decisions effectively. This research implements a web-based Decision Support System to provide recommendations for tourist destinations in Bogor using the User-Based Collaborative Filtering method with cosine similarity. The system is developed using Python and Flask framework, analyzing similarity patterns of ratings between users to generate personal recommendations. The collaborative filtering method works by identifying users who have similar preferences, then recommending tourist destinations that are favored by users with similar preference characteristics. System performance evaluation using RMSE and MAE metrics shows a good level of prediction accuracy. This system helps tourists obtain more personalized and efficient recommendations, and can be utilized by tourism managers as a data-driven promotional tool. The implementation of this system is expected to improve tourist experience in choosing destinations and support the development of the tourism sector in Bogor
References
[1] Alatas, M. A., & Syafitra, D. (2025). Perancangan Sistem Penjadwalan Dan Penggajian Karyawan Toko Kebaya Amora Berbasis Web Metode Extreme Programming. 3(2), 1–10. https://mypublikasi.com/
[2] Aliman, W. (2021). Perancangan Perangkat Lunak untuk Menggambar Diagram Berbasis Android. Syntax Literate ; Jurnal Ilmiah Indonesia, 6(6), 3091. https://doi.org/10.36418/syntax-literate.v6i6.1404
[3] Ambara, I. G. Y. A., Paramitha, A. A. I. I., & Putri, I. G. A. P. D. (2024). Pengembangan Website Desa Wisata Sebagai Media Informasi Wisatawan Pada Desa Temesi. AJAD : Jurnal Pengabdian Kepada Masyarakat, 4(2), 408–414. https://doi.org/10.59431/ajad.v4i2.354
[4] Azzahra, Z. F., & Anggoro, A. D. (2022). Analisis Teknik Entity-Relationship Diagram dalam Perancangan Database Sebuah Literature Review. INTECH (Informatika dan Teknologi), 3(2), 70-74. Jurnal Intech, 3(2), 18–22.
[5] Cahyani, P. R., Maylinasari, L., Ambami, S. A., & Putra, B. R. (2023). Analisis Dan Desain Sistem Aplikasi Kantin Elektronik (E-Canteen) Bagi Mahasiswa Dan Staff Universitas. Journal of Digital Business and Innovation Management, 2(2), 164–179. https://doi.org/10.26740/jdbim.v2i2.58084
[6] Candra Wijayanto, & Yeremia Alfa Susetyo. (2022). Implementasi Flask Framework Pada Pembangunan Aplikasi Sistem Informasi Helpdesk (SIH). JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika), 07(03), 858–868.https://jurnal.stkippgritulungagung.ac.id/index.php/jipi/article/view/3161/1328
[7] Farhan Abiyyu, I., Zulfia Zahro’, H., & Dedy Irawan, J. (2023). Sistem Pendukung Keputusan Untuk Penentuan Pelaksanaan Praktikum Terbaik Menggunakan Metode Topsis. JATI (Jurnal Mahasiswa Teknik Informatika), 7(1), 890–898. https://doi.org/10.36040/jati.v7i1.6189
[8] F. Nugroho and M. Ismu Rahayu, “SISTEM REKOMENDASI PRODUK UKM DI KOTA BANDUNG MENGGUNAKAN ALGORITMA COLLABORATIVE FILTERING,” J. Ris. Sist. Inf. dan Teknol. Inf., vol. 2, no. 3, pp. 23–31, Sep. 2020, doi: 10.52005/jursistekni.v2i3.63.
[9] Hafiz Maulana Siagian, Nasution, M. I. P., & Triase. (2022). IMPLEMENTASI FRAMEWORK BOOTSTRAP PADA SISTEM KERJA PRAKTEK BERBASIS WEB RESPONSIVE. JSiI (Jurnal Sistem Informasi), 9(1), 6–11. https://doi.org/10.30656/jsii.v9i1.3922
[10] Herwanto, A. (2025). Analisis Perbandingan Kinerja Browser : Studi Kasus Google Chrome, Mozilla Firefox. RIGGS: Journal of Artificial Intelligence and Digital Business, 4(3), 3545–3549. https://doi.org/10.31004/riggs.v4i3.2512
[11] K. Obajha, N. N. K. Sari, and V. H. Pranatawijaya, “Implementasi Metode Collaborative Filtering pada Aplikasi Rekomendasi Hotel dan Wisma di Kota Palangka Raya Berbasis Website,” KONSTELASI Konvergensi Teknol. dan Sist. Inf., vol. 3, no. 2, pp. 398–410, Dec. 2023, doi: 10.24002/konstelasi.v3i2.7133.
[12] M. Minarni and S. Sigit, “Pengembangan Sistem Informasi Rekomendasi Wisata Kotawaringin Timur Berbasis Web MenggunakanMetode Item-Based Collaborative Filtering,” J. Ilm. Inform. Glob., vol. 13, no. 3, Jan. 2023, doi: 10.36982/jiig.v13i3.2695.
[13] R. Faurina and E. Sitanggang, “Implementasi Metode Content-Based Filtering dan Collaborative Filtering pada Sistem Rekomendasi Wisata di Bali,” Techno.Com, vol. 22, no. 4, pp. 870–881, Nov. 2023, doi: 10.33633/tc.v22i4.8556.
[14] T. E. Tarigan, E. Faizal, and Sumiyatun, “Model Rekomendasi Wisata dengan Pendekatan Collaborative Filtering,” J. Inform. Komputer, Bisnis dan Manaj., vol. 21, no. 2, pp. 56–64, Nov. 2023, doi: 10.61805/fahma.v21i2.18.
[15] Y. S. Pasaribu and Timotius Selar Sitompul, “Rekomendasi Destinasi Wisata Kota Bandung Menggunakan Algoritma Collaborative Filtering,” Mutiara J. Penelit. dan Karya Ilm., vol. 1, no. 6, pp. 382–392, Dec. 2023, doi: 10.59059/mutiara.v1i6.736.






