YouTube Comment Clustering Using K-Means in A Case Study of The Indonesian New Capital City (IKN) - Nova, Sausan Hidayah; Rizki, Afian Syafaadi; Wibowo, Dwi Agung; Ridha, M Najamudin; Karima, Cahya; Permatasari, Nindy (LPPM Universitas Dian Nuswantoro, 2025)
The relocation of the capital city of the Republic of Indonesia from Jakarta to the Nusantara Capital City (IKN) is a critical topic for the public, as it is designated as a strategic national project. However, the lack of public participation may generate community concerns regarding its potential impact. This research involved extracting public opinion from YouTube comments to identify the community’s desires, thereby providing policymakers with valuable information. Clustering the comments using the K-Means method successfully extracted public opinions from 27,063 comment data points. Among the key findings, a significant public concern is the potential for the construction project to be abandoned or stalled (“mangkrak”). Additionally, while the clustering results showed good cohesion, the cluster separation indicated a significant overlap in the data. This is further reflected by the average similarity score of 0.4234972. Keywords – YouTube, Text Clustering, K-Means, Nusantara Capital City (IKN)