Career Pattern Analysis of SMKN 1 Stabat Graduates Using K-Means Clustering Algorithm on Tracer Study Dataset

Authors

  • Ibrahim Ibrahim Pascasarjana, Magister Teknologi Informasi, Universitas Pembangunan Pancabudi, Medan, Indonesia
  • Muhammad Iqbal Pascasarjana, Magister Teknologi Informasi, Universitas Pembangunan Pancabudi, Medan, Indonesia

Keywords:

Tracer Study, K-Means Clustering, Rapidminer, Career Pattern, Graduates, Data Analysis

Abstract

Tracer study is a method commonly used to determine the condition of graduates of an educational institution, including the career patterns they pursue. This study aims to analyze the career patterns of SMKN 1 Stabat graduates by utilizing the K-Means clustering algorithm. The dataset was obtained from the results of a tracer study of 287 alumni of SMKN 1 Stabat. The dataset used came from a tracer study conducted on graduates in the last five years. By grouping data using K-Means, it is hoped that specific patterns can be found that can help schools improve the quality of learning and student work readiness.[4] The results of the analysis show several dominant career pattern groups, such as the industrial sector, entrepreneurship, and further education.

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Published

2024-07-28

How to Cite

Ibrahim, I., & Muhammad Iqbal. (2024). Career Pattern Analysis of SMKN 1 Stabat Graduates Using K-Means Clustering Algorithm on Tracer Study Dataset. BIOS: Jurnal Informatika Dan Sains, 2(01), 117–128. Retrieved from https://seaninstitute.or.id/bersinar/index.php/bios/article/view/154

Issue

Vol. 2 No. 01 (2024): BIOS: Jurnal Informatika dan Sains

Section

Articles