Application of K-Means and C4.5 Algorithms for dropout risk prediction in vocational high schools - Anwar, Syahrul (International Journal Labs, 2025)
The issue of dropout remains a serious challenge at the vocational high school (SMK) level, including at SMK Islam Nusantara. Over the past five years, the school has experienced a dropout rate of 20% of all enrolled students. This study aims to identify students at high risk of dropping out by applying the K-Means and C4.5 algorithms. The K-Means algorithm is used to cluster students based on factors such as academic performance, socioeconomic conditions, and the distance from home to school. Subsequently, the C4.5 algorithm is used to predict dropout risk based on the clustering results. The data used in this study were obtained directly from the Dapodik Data Center of SMK Islam Nusantara and include student data from the 2019 to 2024 academic years. The results indicate that socioeconomic factors, the distance from home to school, and academic performance significantly influence dropout risk. Students from low-income families and with poor academic performance were found to be at the highest risk of dropping out. This study makes a significant contribution to SMK Islam Nusantara by developing an early warning system that can help identify students at risk of dropping out. With more targeted interventions, such as academic counseling and socioeconomic support, it is expected that the dropout rate can be significantly reduced. Additionally, this predictive model can be applied to other vocational schools with similar conditions to improve the overall quality of vocational education.