Prediction of Employment Status of Vocational High School Graduates Using Random Forest Algorithm: Academic, Social and Family Multifactor Analysis
Prediksi Status Pekerjaan Lulusan SMK Menggunakan Algoritma Random Forest: Analisis Multifaktor Akademis, Sosial dan Keluarga
DOI:
https://doi.org/10.21070/ups.8080Keywords:
Random Forest, SMK, Employment Status, Prediction, Family Factors, machine learningAbstract
This study aims to develop a prediction model for the employment status of senior high school (SMK) graduates in Indonesia using multifactor analysis involving academic performance, social environment, and family. This study uses a quantitative approach with the Random Forest algorithm to collect large amounts of data and provide specific predictions. The model predicts the employment status of SMK graduates by 76%, indicating good work performance. This study also found that significant community factors significantly affect the employment status of SMK graduates (38.9%), followed by social factors (36.8%) and academic factors (24.5%). This study encourages schools, parents, and the government to focus on holistic SMK education, such as collaboration between schools and industry, to improve the employment status of SMK graduates.
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