Perbandingan Algoritma Greedy dan Dynamic Programming Pada Optimasi Playlist Spotify Untuk Jogging
DOI:
https://doi.org/10.58466/1htfcz49Kata Kunci:
Optimasi Playlist Spotify, Algoritma Greedy, Dynamic Programming, 0/1 Knapsack Problem, Rekomendasi MusikAbstrak
Spotify menyediakan metadata audio yang dapat dimanfaatkan untuk mendukung aktivitas olahraga seperti jogging. Penelitian ini membandingkan performa algoritma Greedy dan Dynamic Programming dalam optimasi playlist Spotify yang dimodelkan sebagai permasalahan 0/1 Knapsack. Durasi lagu direpresentasikan sebagai bobot, sedangkan skor yang dibentuk dari kombinasi popularitas dan energi digunakan sebagai nilai. Dataset diperoleh dari Spotify Wrapped 2025 Top 50 Songs dan Spotify All-Time Top 100 Songs yang setelah preprocessing dan filterisasi menghasilkan 31 lagu kandidat. Pengujian dilakukan pada lima skenario durasi playlist, yaitu 30, 45, 60, 75, dan 90 menit. Hasil penelitian menunjukkan bahwa Dynamic Programming secara konsisten menghasilkan total score yang lebih tinggi dibandingkan Greedy pada seluruh skenario pengujian. Pada playlist berdurasi 60 menit, Dynamic Programming memperoleh total score sebesar 1897, sedangkan Greedy memperoleh 1894. Namun, Greedy memiliki waktu eksekusi yang lebih rendah, yaitu 4,244 ms dibandingkan 16,196 ms pada Dynamic Programming. Nilai rata-rata optimality gap yang diperoleh sebesar 1,89%, menunjukkan bahwa solusi yang dihasilkan Greedy sangat mendekati solusi optimal Dynamic Programming dengan kebutuhan komputasi yang lebih rendah.
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Hak Cipta (c) 2026 Fadhel Muhammad , Muhammad Radja Juang Jamemiko, Yohannes Yohannes
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