Implementasi Metode K-Means Pada Popularitas Lagu Berdasarkan Data Berbagai Platform

The development of digital technology has made it easier for people to access music through various platforms such as Spotify, YouTube, and TikTok. However, each platform uses different indicators to measure song popularity, making it difficult for users to identify which songs are currently trending across platforms. This study aims to process song data from multiple digital music platforms and classify song popularity levels using the K-Means Clustering method. In addition, a survey was conducted to identify users' perceptions of the difficulties they face in finding trending songs. The research data were collected from various digital music platforms, including indicators such as the number of listeners, the number of views, the frequency of song usage, and other supporting attributes. The data were then processed using the K-Means algorithm to classify songs into several categories based on their popularity levels. The results show that the K-Means method successfully grouped songs into three categories: high, medium, and low popularity. This classification simplifies the process of identifying trending songs without requiring users to compare data across multiple platforms individually. The survey results also indicate that most respondents experience difficulties in finding information about popular songs because they need to access several platforms. Therefore, the implementation of the K-Means Clustering method can help integrate and classify song popularity data, making the information easier to understand and more useful for users.