Analisis Sentimen Opini Publik Terkait Kebijakan Royalti Musik Menggunakan Algoritma Naïve Bayes pada Data Twitter

Nilna Sholihah (Program Studi Magister Pengembangan Sumber Daya Manusia, Sekolah Pascasarjana, Universitas Airlangga)
Mas Rahmah (Program Studi Magister Pengembangan Sumber Daya Manusia, Sekolah Pascasarjana, Universitas Airlangga)

Jurnas Nasional Teknologi dan Sistem Informasi
Vol 12 No 2 (2026): Agustus 2026

The music royalty policy in Indonesia, regulated by the Minister of Law Regulation Number 27 of 2025, has triggered various responses from the public. This study aims to analyze public opinion sentiment on Twitter regarding the music royalty policy using the Naive Bayes algorithm. Data was collected from Indonesian-language Twitter accounts from August 1-15, 2025, resulting in 1,588 tweets, which were then analyzed using Orange Data Mining. The analysis process included data crawling using Google Colab, data preprocessing, emotional analysis, and polarization analysis using multilingual sentiment. The results showed a distribution of sentiment: 584 positive tweets (36.8%), 561 neutral tweets (35.3%), and 443 negative tweets (27.9%). The Naive Bayes algorithm produced an accuracy of 97.17% with a prediction error rate of 45 tweets. The confusion matrix demonstrated excellent model performance in classifying public sentiment. This study provides important insights into public perceptions of the implementation of the music royalty policy and can be used as evaluation material for stakeholders in the Indonesian music industry.