Implementasi Metode Content-Based Filtering dan Collaborative Filtering pada Sistem Rekomendasi Wisata di Bali
DOI:
https://doi.org/10.33633/tc.v22i4.8556Keywords:
sistem rekomendasi, destinasi wisata, Bali, content-based, collaborative filteringAbstract
Sektor pariwisata memiliki peran penting dalam perekonomian Bali. Pada bulan April 2023, kunjungan wisatawan ke Bali mencapai 411.510, meningkat 11,01% dari bulan Maret 2023 (sumber: Badan Pusat Statistik Bali). Untuk memperkenalkan destinasi wisata yang ada, Bali perlu menggunakan teknologi yang sedang berkembang seperti sistem rekomendasi. Dalam hal ini, digunakan metode Content-based filtering (CBF) dan Collaborative Filtering (CF). CBF memberikan rekomendasi berdasarkan preferensi pengguna terhadap kategori destinasi wisata, sementara CF menggunakan data histori rating dari pengguna lain untuk merekomendasikan destinasi yang disukai. Dataset terdiri dari 75 data detail destinasi wisata dan 3000 histori rating dari 100 pengguna. Pengujian dilakukan dengan membagi dataset menjadi 80% data training (2400 data) dan 20% data validasi (600 data), menggunakan 15 epoch dan batch size yang sesuai. Hasil terbaik menunjukkan performa loss sebesar 0.0589 dan RMSE sebesar 0.2427.References
BPS, “Peningkatan kunjungan wisatawan mancanegara pada April 2023 yang tumbuh 276,31 persen dibandingkan April 2022 dan Jumlah penumpang angkutan laut dalam negeri pada April 2023 naik 24,75 persen,” 2023. https://www.bps.go.id/pressrelease/2023/06/05/1978/peningkatan-kunjungan-wisatawan-mancanegara-pada-april-2023-yang-tumbuh-276-31-persen-dibandingkan-april-2023-dan-jumlah-penumpang-angkutan-laut-dalam-negeri-pada-april-2023-naik-24-75-persen.html.
BPS Bali, “Jumlah Wisatawan Asing ke Indonesia dan Bali, 1969-2022,” 2023. https://bali.bps.go.id/statictable/2018/02/09/28/jumlah-wisatawan-asing-ke-bali-dan-indonesia-1969-2019.html.
F. Ricci, L. Rokach, and B. Shapira, Recommender Systems Handbook. 2011.
R. Nugroho, A. Polina, and Y. Mahendra, “Tourism Site Recommender System Using Item-Based Collaborative Filtering Approach,” Int. J. Appl. Sci. Smart Technol., vol. 2, no. 2, pp. 119–126, 2020, doi: 10.24071/ijasst.v2i2.2987.
H. J. Jun, J. H. Kim, D. Y. Rhee, and S. W. Chang, “‘SeoulHouse2Vec’: An embedding-based collaborative filtering housing recommender system for analyzing housing preference,” Sustain., vol. 12, no. 17, 2020, doi: 10.3390/SU12176964.
M. I. Mi’Roj, “Muhammad Ilhamil Mi’Roj pada tahun 2023, yang mengaplikasikan metode Item-Based Collaborative Filtering untuk membangun sistem rekomendasi pariwisata di Kabupaten Sidoarjo,” 2023.
M. Naufal et al., “Implementasi Sistem Rekomendasi Makanan pada Aplikasi EatAja Menggunakan Algoritma Collaborative Filtering,” J. MULTINETICS, vol. 7, no. 2, pp. 177–185, 2021.
T. D. A. Mufidatul Islamiyah, Puji Subekti, “Pemanfaatan Metode Based Collaborative Filtering Untuk Rekomendasi Wisata Di Kabupaten Malang,” J. Teknol. Inf. dan Ilmu Komput., vol. 13, no. 2, 2019, doi: https://doi.org/10.32815/jitika.v13i2.70.
F. Ricci, L. Rokach, B. Shapira, P. B. Kantor, and F. Ricci, Recommender Systems Handbook 123. 2011.
V. Mellville, P and Sindhwani, “Recomender Systems: Encyclopedia of machine learning ch:00338,” IBM Res. Cent., 2010.
H. T. Y. Achsan, H. Suhartanto, W. C. Wibowo, D. A. Dewi, and K. Ismed, “Automatic Extraction of Indonesian Stopwords,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 2, pp. 166–171, 2023, doi: 10.14569/IJACSA.2023.0140221.
J. Kaur and P. Kaur Buttar, “A Systematic Review on Stopword Removal Algorithms,” Int. J. Futur. Revolut. Comput. Sci. Commun. Eng., April, pp. 207–210, 2018, [Online]. Available: http://www.ijfrcsce.org.
S. A. Amira and M. I. Irawan, “Opinion Analysis of Traveler Based on Tourism Site Review Using Sentiment Analysis,” IPTEK J. Technol. Sci., vol. 31, no. 2, p. 223, 2020, doi: 10.12962/j20882033.v31i2.6338.
D. A. Nasution, H. H. Khotimah, and N. Chamidah, “Perbandingan Normalisasi Data untuk Klasifikasi Wine Menggunakan Algoritma K-NN,” Comput. Eng. Sci. Syst. J., vol. 4, no. 1, p. 78, 2019, doi: 10.24114/cess.v4i1.11458.
R. H. Mondi and A. Wijayanto, “Recommendation System With Content-Based Filtering Method for Culinary Tourism in Mangan Application,” ITSMART J. Ilm. Teknol. dan Inf., vol. 8, no. 2, pp. 65–72, 2019.
I. G. A. G. A. Kadyanan, “Perancangan Sistem Rekomendasi dalam Industri Kuliner di Bali,” J. Ilm., vol. X, no. 1, pp. 1–6, 2017.
M. A. S. R. E. Nakhli, H. Moradi, “Movie Recommender System Based on Percentage of View,” Knowledge-Based Eng. Innov. - KBEI, pp. 656–660, 2019, doi: 10.1109/KBEI.2019.8734976.
Downloads
Published
Issue
Section
License
License Terms
All articles published in Techno.COM Journal are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). This means:
1. Attribution
Readers and users are free to:
-
Share – Copy and redistribute the material in any medium or format.
-
Adapt – Remix, transform, and build upon the material.
As long as proper credit is given to the original work by citing the author(s) and the journal.
2. Non-Commercial Use
-
The material cannot be used for commercial purposes.
-
Commercial use includes selling the content, using it in commercial advertising, or integrating it into products/services for profit.
3. Rights of Authors
-
Authors retain copyright and grant Techno.COM Journal the right to publish the article.
-
Authors can distribute their work (e.g., in institutional repositories or personal websites) with proper acknowledgment of the journal.
4. No Additional Restrictions
-
The journal cannot apply legal terms or technological measures that restrict others from using the material in ways allowed by the license.
5. Disclaimer
-
The journal is not responsible for how the published content is used by third parties.
-
The opinions expressed in the articles are solely those of the authors.
For more details, visit the Creative Commons License Page:
? https://creativecommons.org/licenses/by-nc/4.0/
