SISTEM REKOMENDASI BERITA DENGAN METODE CONTENT-BASED FILTERING

Penulis

DOI:

https://doi.org/10.31000/jika.v9i2.13247

Abstrak

Pertumbuhan jumlah konten digital yang meningkat terus setiap harinya menimbulkan tantangan dalam penyaringan dan penyajian informasi yang relevan kepada pengguna internet. Diperlukan adanya sistem rekomendasi yang mampu membantu pengguna dalam menyaring informasi secara relevan. Penelitian ini bertujuan untuk mengembangkan sistem rekomendasi artikel berita berbasis content-based filtering dengan algoritma cosine similarity. Dataset penelitian ini terdiri dari 150 artikel berita yang diambil melalui platform Kaggle. Tahap pre-processing data mencakup normalisasi data, pembersihan data, penghapusan entri duplikat, penghapusan stop words, dan tokenisasi. Pengujian dilakukan dengan mengambil 15 sampel artikel, masing-masing menghasilkan lima rekomendasi berdasarkan skor kemiripan tertinggi. Hasil penelitian menunjukkan bahwa sistem mampu memberikan rekomendasi konten artikel berita untuk kebutuhan pengguna. Tahap pre-processing data terbukti berperan penting dalam meningkatkan kualitas rekomendasi.

Referensi

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Unduhan

Diterbitkan

2025-04-30

Terbitan

Vol 9 No 2 (2025): JIKA (Jurnal Informatika)

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Articles

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