Abstract
This study aims to analyze and identify patterns in the educational backgrounds of successful figures using the K-Means clustering method. Educational background is one of the factors that can provide an overview of an individual's academic journey toward achieving success. However, the diversity of educational fields, levels of education, and academic characteristics of successful figures makes these patterns difficult to analyze manually. Therefore, a data clustering method is needed to help identify similarities in the educational characteristics of successful figures. This study uses a quantitative approach with data collected from various sources regarding the educational backgrounds of successful figures, including education level, field of study, educational institution, and other educational characteristics. The collected data are then processed and transformed to make them suitable for the clustering process. The K-Means method is used to group successful figures based on the similarity of their educational background characteristics. The research process includes data collection, preprocessing, determination of the number of clusters, centroid initialization, calculation of the distance between each data point and the centroid, cluster formation, and evaluation of the clustering results. The clustering results are expected to reveal several groups of educational patterns with similar characteristics and provide information regarding trends in the educational backgrounds of successful figures. This study is expected to serve as a reference for understanding the diversity of educational patterns among successful figures and provide an overview of the relationship between educational characteristics and an individual's journey toward success.
