Analisis Topic-Modelling Menggunakan Latent Dirichlet Allocation (LDA) Pada Ulasan Sosial Media Youtube

 (*)Vika Alpiana Mail (Universitas Dian Nuswantoro, Semarang, Indonesia)
 Abu Salam (Universitas Dian Nuswantoro, Semarang, Indonesia)
 Farrikh Alzami (Universitas Dian Nuswantoro, Semarang, Indonesia)
 Ifan Rizqa (Universitas Dian Nuswantoro, Semarang, Indonesia)
 Diana Aqmala (Universitas Dian Nuswantoro, Semarang, Indonesia)

(*) Corresponding Author

Submitted: December 12, 2023; Published: January 10, 2024


This research explores the role of Micro, Small, and Medium Enterprises (MSMEs) in the Indonesian economy, focusing on sales and marketing challenges in the era of social media, especially YouTube. With millions of individuals using this platform to share product insights, reviews, and experiences, MSMEs need to receive relevant feedback. This study applies text mining, particularly the topic modeling analysis method with Latent Dirichlet Allocation (LDA), to analyze user comments on MSME videos, with an emphasis on Lumpia Gang Lombok Semarang on YouTube. Through the application of LDA, the identification of ten main topics is conducted, with the highest coherence value reaching 0.414027. The visualization of the intertopic distance map provides an understanding of the relationships between topics and dominant words. Comment analysis provides valuable insights into user preferences and perceptions of products, supporting MSMEs in understanding customer satisfaction and enhancing value for those enterprises. These findings also affirm the effectiveness of YouTube as a relevant data source for understanding public preferences for MSME products. This research details text processing methods, including extraction, cleaning, tokenization, normalization, removal of stopwords, and stemming. With this approach, the research not only provides insights into topic analysis in the context of social media but also makes a valuable contribution to the development and marketing of MSMEs through a better understanding of social media data, especially on the YouTube platform.


MSMEs; Topic Modeling Analysis; Latent Dirichlet Allocation (LDA); YouTube; User Preferences

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