IMPLEMENTASI KINERJA ALGORITMA MULTI-ARMED BANDIT (MAB) UNTUK OPTIMALISASI REKOMENDASI KONTEN VIDEO PENDEK

Suherwin Suherwin

doi:10.33506/insect.v12i01.5425

Authors

Suherwin Suherwin Program Studi Teknik Informatika, Universitas Pejuang Republik Indonesia

DOI:

https://doi.org/10.33506/insect.v12i01.5425

Keywords:

MAB, Rekomendasi, Video Pendek, Preferensi Pengguna, Epsilon-Greedy

Abstract

Penelitian ini bertujuan untuk mengeksplorasi penerapan algoritma Multi-Armed Bandit (MAB) dalam sistem rekomendasi video pendek berbasis preferensi pengguna. Algoritma MAB, yang dikenal dalam konteks pemilihan keputusan sekuensial, digunakan untuk meningkatkan relevansi rekomendasi video dengan cara yang adaptif dan dinamis. Dengan menggunakan pendekatan epsilon-greedy, algoritma ini menyesuaikan rekomendasi video secara real-time berdasarkan umpan balik dari pengguna, seperti klik, like, dan durasi tonton. Penelitian ini menguji kinerja sistem rekomendasi dengan data uji yang mencakup interaksi pengguna terhadap lima video pendek. Hasil penelitian menunjukkan bahwa MAB berhasil menghasilkan rekomendasi yang lebih relevan, meningkatkan tingkat keterlibatan pengguna, dan memberikan solusi personalisasi yang lebih baik dibandingkan dengan sistem rekomendasi tradisional. Evaluasi sistem dilakukan menggunakan metrik seperti jumlah klik, waktu tonton rata-rata, dan tingkat keterlibatan, yang menunjukkan bahwa MAB dapat secara efektif menyesuaikan rekomendasi dengan perubahan preferensi pengguna. Penelitian ini memberikan kontribusi dalam pengembangan sistem rekomendasi berbasis algoritma MAB, dengan potensi untuk diterapkan pada platform berbagi video pendek lainnya.

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