Hierarchical And Non-Hierarchical Algorithm Clustering: 21st Century Skill Grouping In State Vocational Schools Students

Rahadyan Tajuddien, Wahyu Nurul Faroh

Abstract


Vocational High Schools (SMK), which initially aimed to produce ready-to-work graduates, is the biggest contributor to unemployment. Efforts to overcome this, SMK graduates need to be given 21st century skills to increase agility to survive in the current industrial 4.0 era. 21st century skills are described as: life and career skills, learning and innovation skills and information media and technology skills.

This study conducted a sample grouping of the data from 10,737 students of State Vocational High Schools in Bogor Regency who took advantage of 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 algorithms used for cluster determination are hierarchical and non-heirarchic (K-Means) algorithms. Hierarchical algorithm is a method that does not determine the number of clusters, so that the result or output of a dendogram with a certain number of clusters is determined by the distance until only 2 clusters are formed. K-Means is a non-hierarchical clustering data method that can group student data into several clusters based on the similarity of these data, so that student data with the same characteristics are grouped into one cluster and those with different characteristics are grouped into other clusters. SPSS 25 is used to help find accurate values. The attributes used are life and career skills, learning and innovation skills and information media and technology skills.

The results showed that life and career skills had a frequency of 4,130 students or 38%, learning and innovation skills had a frequency of 4,977 students or 46% and information media and technology skills had a frequency of 3,796 students or 35%. Meanwhile, 21st century skills at 11 SMKN in Bogor Regency are described in stages. The results of Non-Hierarchical Clustering are described in three ways. Furthermore, this research can be used as a basis for decision making to determine future Vocational School development strategies.

Keywords: Clustering, Hierarchical Algorithm, Non-Hierarchical K-Means Algorithm, 21st Century Skills, SMKN


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