Best Programming Creator Content Selection with K-Means Clustering Algorithm and MAUT Method - Aryunani, Witari; Setiani, Yeni; Purnama, Indra (Building of Informatics, Technology and Science (BITS), 2025)
Selecting quality programming content creators on platforms such as YouTube is becoming a complex challenge as digital educational content expands. This research designs a systematic approach by combining K-Means algorithm and MAUT method to objectively evaluate and rank creators. Data from 100 programming channels was analysed using K-Means, resulting in three main clusters based on audience views and interactions. The leading cluster was identified with an average of 335,461 views per video and an engagement rate of 0.31%. The MAUT method then ranked the creators in this cluster, revealing Brackeys as the best programming contentcreator with an optimal balance between audience reach and participation with a final score of 0.624. The results show that the integration of these two methods is effective in providing a data-driven solution for educational content selection, as well as a reference for creators in improving the quality of the material. The combination of K-Means and MAUT not only answers the need for objectivity in content curation, but also enriches the literacy of multidimensional evaluation methods in the era of online learning.