Determining Characteristics of Popular Local Songs in Indonesia's Music Market

Limisgy Ramadhina Febirautami, Isti Surjandari Prajitno, Enrico Laoh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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%.

Original languageEnglish
Title of host publicationProceedings - 2018 5th International Conference on Information Science and Control Engineering, ICISCE 2018
EditorsYun Cheng, Shaozi Li, Ying Dai
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages197-201
Number of pages5
ISBN (Electronic)9781538655009
DOIs
Publication statusPublished - 14 Jan 2019
Event5th International Conference on Information Science and Control Engineering, ICISCE 2018 - Zhengzhou, Henan, China
Duration: 20 Jul 201822 Jul 2018

Publication series

NameProceedings - 2018 5th International Conference on Information Science and Control Engineering, ICISCE 2018

Conference

Conference5th International Conference on Information Science and Control Engineering, ICISCE 2018
CountryChina
CityZhengzhou, Henan
Period20/07/1822/07/18

Keywords

  • CART
  • data mining
  • decision tree
  • machine learning
  • music industry
  • popular songs

Fingerprint Dive into the research topics of 'Determining Characteristics of Popular Local Songs in Indonesia's Music Market'. Together they form a unique fingerprint.

Cite this