Determining characteristics of popular local songs in Indonesia’s music market
Published in IEEE 2018 5th International Conference on Information Science and Control Engineering (ICISCE), 2018
The music industry in Indonesia has the potential to improve its competitiveness. However, there are challenges in how to improve the competitiveness. Nowadays, the internet, especially the developing online streaming service, provides the music industry to be able to promote and publish music or songs. The online streaming service provides accessible data, e.g. TOP Charts data and audio features, with opportunities to analyze the data in order to help create a new marketing strategy. These data may help the music industry know hidden information in local songs or music markets. However, research based on data in popular local songs, particularly using more understandable audio features provided by Spotify to know the characteristics distinguishing popular songs to non-popular songs has not been conducted. In this study, CART decision tree was conducted aiming to determine the characteristics of popular local songs in Indonesia’s music market. The local songs from the Daily TOP 200 Spotify were collected and divided into 163 training data songs and 70 test data songs. From the results, there were several attributes representing the characteristics of popular local songs. The evaluation of the model resulted in an accuracy of 72.8%.