The Clustering YouTube Videos of SMK Negeri 1 Percut Sei Tuan Based on Views and Likes Using the K-Means Algorithm
DOI:
https://doi.org/10.65126/jocosir.v2i2.59Keywords:
YouTube analytics, K-Means clustering, audience engagement, educational content, data miningAbstract
The increasing use of YouTube as a digital learning and promotional platform has encouraged educational institutions to optimize their content strategies to enhance audience engagement. This study aims to analyze and categorize YouTube videos from SMK N 1 Percut Sei Tuan based on views and likes using the K-Means clustering algorithm. A total of 50 videos were collected and preprocessed using normalization techniques to ensure consistent data scaling. The optimal number of clusters was determined using the Elbow Method, resulting in three distinct engagement groups: high, medium, and low. The clustering process was implemented using Python with the support of the pandas, numpy, scikit-learn, and matplotlib libraries. The results show that videos categorized under high engagement typically consist of school achievements and major institutional events, while low-engagement videos are related to administrative or routine activities with limited public appeal. The clustering outcomes provide valuable insights into audience preferences, allowing educational institutions to improve future content strategies by focusing on video types that generate higher engagement. This research demonstrates that the K-Means algorithm is effective in identifying content patterns and can be used as a decision-support tool for optimizing YouTube channel growth in the educational sector.
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