Clustering Hierarchical and Non-Hierarchical Algorithm: Clustering of 21st Century Hr Skillsi In SMKN Students In Indonesia - Wahyu Nurul Faroh; Rahadyan Tajuddien; Umi Narimawati; Azhar Affandi; Sidik Priadana (Peneliti.net, 2022)
This study conducted a grouping sample of the data of State Vocational High School students in Bogor Regency by utilizing the data mining process using clustering techniques. The method in this study uses the Cross-School Standard Process for Data Mining (CSISP-DM). While the algorithm used for cluster determination is a hierarchical and non-heirical algorithm (K-Means). The hierarchical algorithm is a method that does not specify the number of clusters so that the results or output of a dendogram with a certain number of clusters are determined by the distance until only 2 clusters are formed, while K-Means is a non-hierarchical clustering data method that can group student data into several clusters based on the similarity of the data, so that student data with the same characteristics are grouped in one cluster and those with different characteristics are grouped in another cluster. The attributes used are life and career skills (life and career skills), learning and innovation skills (learning and innovation skills) and information media and technology skills. The results of this study we can conclude that the percentage of 21st century skills in students with a sample of 10737 students at SMKN Bogor is life and career skills (life and career skills) 38% with a frequency of 4130 students, learning and innovation skills (learning and innovation skills) 46 % with a frequency of 4977 Students and information media and technology skills 35% with a frequency of 3796 Students