Role of Recommendation Systems in Digital Music Streaming Platforms: User Decision-Making Among Sampoerna University Students

Abstrak

This study explored the influence of recommendation systems in digital music streaming platforms on the decision-making processes of students at Sampoerna University. Utilizing a socio-technical framework, the research examined the tension between algorithmic efficiency and user exploration. Data were collected via a quantitative survey of 41 students, which revealed that while platforms like Spotify (80.5%) and YouTube Music (51.2%) were dominant, users experienced a distinct "algorithmic paradox." Although the most frequent rating for recommendation accuracy was an 8 out of 10 (reported by 41.5% of respondents), a significant 68.3% of students reported experiencing algorithmic boredom due to repetitive suggestions. These findings suggested that current systems, while effective at reducing cognitive load through features like auto-play (68.3%) and personalized playlists (53.7%), often trapped users in "filter bubbles" or "taste tautologies." The study concluded that the next generation of music Information System must evolve beyond simple predictive accuracy to incorporate context-aware diversity and serendipity, thereby balancing the efficiency of automation with the human need for discovery and autonomy.