Prediksi Kelayakan Pendidikan Sekolah Dasar di Indonesia Menggunakan Metode Random Forest Berbasis Python Tahun 2023–2024
Keywords:
education feasibility, Indonesia primary school, machine learning, random forest, data scienceAbstract
This study aims to predict the feasibility of primary school education in Indonesia for the 2023–2024 period using a Random Forest algorithm based on Python. The research is motivated by disparities in educational quality across regions, influenced by factors such as infrastructure, teacher availability, and student performance indicators. The dataset used includes variables such as number of students, teachers, school principals, study groups, dropout rates, repetition rates, and classroom conditions across 38 provinces. The method involves data preprocessing, model training, and evaluation using accuracy, precision, recall, and F1-score metrics. The findings show that the Random Forest model achieves high predictive performance and is effective in identifying key factors influencing educational feasibility, particularly classroom conditions and teacher availability. The study implies that machine learning can support data-driven decision-making in education policy to improve equity and quality across regions.
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