Classification of Vacational High School Graduates’ Ability in Industry using Extreme Gradient Boosting (XGBoost), Random Forest And Logistic Regression
Klasifikasi Kemampuan Lulusan SMK di Industri Menggunakan Extreme Gradient Boosting (XGBoost), Random Forest dan Logistic Regresssion
DOI:
https://doi.org/10.21070/ups.734Keywords:
Classification, Graduate Quality, Machine Learning, Vocational High School (SMK)Abstract
The world of education is one of the main sources in presenting Human Resources. Vocational High School is one of the school levels that presents various majors that are ready to compete in the industrial world. therefore a school institution needs to have a system to find out how much the quality of education provided to students is able to compete in the industrial world. The goal is that school institutions can strategize to produce better quality students in the following year. In this study there are 4 classes, namely working, not working, students, and entrepreneurs. There are several stages in building a classification system including the preprocessing, processing and evaluation stages. This research uses three algorithms namely XGBoost, Random Forest, and Logistic Regression. The results of the three methods get an accuracy score of 67% produced by the XGBoost and Random Forest algorithms, 50% accuracy score by Logistic Regression
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