Implementation of a Simple Web-Based Recommendation E-Book System Using Content-Based Filtering
https://doi.org/10.58466/aicoms.v4i1.1789
Keywords:
cosine similarity, web performance, Next.js, content-based filtering, E-book recommendationAbstract
The rapid expansion of digital libraries necessitates efficient recommendation systems to help users discover relevant e-books. This study presents the implementation of a simple web-based e-book recommendation system using content-based filtering, developed with the Next.js framework to enhance web loading speed and performance. The system analyzes book metadata and textual content to generate personalized recommendations based on user preferences. Core functionalities include a responsive user interface, book similarity calculations using and cosine similarity, and real-time dynamic suggestions. By leveraging Next.js, the system benefits from server-side rendering (SSR) and static site generation (SSG), ensuring faster page loads and improved user experience. Experimental results indicate that content-based filtering effectively suggests relevant e-books but faces challenges such as the cold-start problem. Future work may integrate hybrid filtering techniques to improve recommendation accuracy and user engagement.
References
D. C. Utomo, V. Atina, and P. Widyaningsih, “PENERAPAN METODE CONTENT BASED FILTERING PADA SISTEM REKOMENDASI PEMILIHAN BUKU REFERENSI RUMAH BELAJAR PANCASILA,” Infotech: Journal of Technology Information, vol. 10, no. 1, pp. 121–128, Jun. 2024, doi: 10.37365/jti.v10i1.262.
C. Zhang, G. Long, T. Zhou, Z. Zhang, P. Yan, and B. Yang, “When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions,” in WWW 2024 - Proceedings of the ACM Web Conference, Association for Computing Machinery, Inc, May 2024, pp. 3632–3642. doi: 10.1145/3589334.3645525.
E. Kannout, M. Grzegorowski, M. Grodzki, and H. S. Nguyen, “Clustering-Based Frequent Pattern Mining Framework for Solving Cold-Start Problem in Recommender Systems,” IEEE Access, vol. 12, pp. 13678–13698, 2024, doi: 10.1109/ACCESS.2024.3355057.
Faris, “Panduan Lengkap Menggunakan Next.js: Framework React untuk Pengembangan Web Modern,” https://soaltekno.lokercepat.id/tutorial-menggunakan-next-js/?utm_source=chatgpt.com.
R. Prasetyo, A. Nugroho, and A. Sugandi, “Meningkatkan Performa Frontend dengan Menggunakan Framework Next.Js dalam Pengembangan Website,” Journal of Cyber Health and Computer, vol. 2, no. 2, 2024, doi: 10.18196/jochac.v3i4.
M. Azis Ramadhan, I. Najiyah, R. Muhammad Abillutfi, R. Musaropah, and N. Dian Pramanik, “IMPLEMENTASI DAN EVALUASI SISTEM REKOMENDASI MUSIK BERBASIS LIRIK DENGAN ALGORITMA TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF).”
T. Ridwansyah, B. Subartini, and S. Sylviani, “Penerapan Metode Content-Based Filtering pada Sistem Rekomendasi,” Mathematical Sciences and Applications Journal, vol. 4, no. 2, pp. 70–77, Apr. 2024, doi: 10.22437/msa.v4i2.32136.
Asa Dilla Safitri, Vihi Atina, and Anisatul Farida, “Sistem rekomendasi buku menggunakan metode content-based filtering,” INFOTECH : Jurnal Informatika & Teknologi, vol. 5, no. 2, pp. 218–227, Dec. 2024, doi: 10.37373/infotech.v5i2.1302.
A. A. Fikhri and N. Nurdin, “IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR PADA SISTEM PEMANTAU SUHU DAN KELEMBAPAN RUANG SERVER MENGGUNAKAN PROTOKOL MQTT BERBASIS IOT,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 3S1, Oct. 2024, doi: 10.23960/jitet.v12i3S1.5422.
L. Cahyani, N. Sephiana, M. Tahir, J. Aisyiah, and S. Artikel, “Jurnal Explore IT|31 Sistem Rekomendasi Wisata Kuliner Madura Menggunakan Content Based Filtering Madura Culinary Tourism Recommendation System Using Content Based Filtering INFO ARTIKEL ABSTRAK,” 2024, doi: 10.35891/explorit.
A. Sanjaya, A. Bagus Setiawan, U. Mahdiyah, I. Nur Farida, A. Risky Prasetyo, and U. Nusantara PGRI Kediri, “PENGUKURAN KEMIRIPAN MAKNA MENGGUNAKAN COSINE SIMILARITY DAN BASIS DATA SINONIM KATA MEASUREMENT OF MEANING SIMILARITY USING COSINE SIMILARITY AND WORD SYNONYMS DATABASE,” vol. 10, no. 4, 2023, doi: 10.25126/jtiik.2023106864.
E. P. Putra, C. Batara, and E. Dephtios, “Perancangan E-Commerce Pada Toko Boxermks Dengan Penerapan Sistem Rekomendasi Menggunakan Metode Cosine Similarity,” 2024.
S. Rahmadhani et al., “JIP (Jurnal Informatika Polinema) SISTEM REKOMENDASI PENELUSURAN BUKU BERBASIS CONTENT-BASED FILTERING DENGAN PEMBOBOTAN TF-RF”.
Angga Risky S, “Belajar Pake Next JS, Bikin Website Jadi Modern!,” https://buildwithangga.com/tips/belajar-pake-next-js-bikin-website-jadi-modern.
I. Dhimas Maulana and Y. A. Susetyo, “Implementasi Fetch API dalam pengembangan Backend Website Daftar Film dengan Next.JS,” 2025.
Murtafi Digital, “Panduan Menggunakan Next.js untuk Desain Website Perusahaan,” https://www.murtafidigital.co.id/panduan-menggunakan-next-js-untuk-desain-website-perusahaan/.
K. R. PUTRA and M. A. RACHMAN, “Perbandingan Metode Content-based, Collaborative dan Hybrid Filtering pada Sistem Rekomendasi Lagu,” MIND Journal, vol. 9, no. 2, pp. 179–193, Dec. 2024, doi: 10.26760/mindjournal.v9i2.179-193.
S. Pati and Y. Zaki, “Evaluating the Efficacy of Next.js: A Comparative Analysis with React.js on Performance, SEO, and Global Network Equity,” Jan. 2025, [Online]. Available: http://arxiv.org/abs/2502.15707
H. H. Ben kora and M. S. Manita, “Modern Front-End Web Architecture Using React.js and Next.js,” University of Zawia Journal of Engineering Sciences and Technology, vol. 2, no. 1, pp. 1–13, Aug. 2024, doi: 10.26629/uzjest.2024.01.
R. I. Prasetyo, “Membaca Pikiran Pengguna dalam Strategi UX/UI (Menciptakan Pengalaman Digital yang Menarik dan Berkesan).” [Online]. Available: https://www.researchgate.net/publication/389494123
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