Analisis Big Data APS SLTA dan Strategi Pendidikan Menggunakan K-Means Berbasis Rapidminer Menuju Indonesia Emas - Khoirun Nisa; Nor Anisa (TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi, 2025)
The Golden Indonesia Vision 2045 places education as the main pillar in creating superior human resources. School Participation Rate (APS) data is an important indicator to evaluate student access and participation in education. This study utilizes the K-Means Clustering method to analyze APS big data to identify patterns of education participation in Indonesia. The results of the analysis show significant participation clusters based on demographic, socio-economic, and geographical factors, and reveal gaps and potential for education improvement in various regions. In this study, RapidMiner is used as an analysis tool to process and visualize APS data. The results of clustering show a striking difference between areas with good access to education and areas with poor access to education. Factors such as income levels, educational infrastructure, and geographical location were found to have a major impact on student participation rates. Strategic recommendations include increasing access to education in disadvantaged areas through equitable distribution of education facilities, infrastructure development, and flexible data-based policies. In addition, scholarship programs in vulnerable areas are also proposed as a solution. This research supports strategic efforts towards the vision of Golden Indonesia 2045 by providing a strong foundation for policies that focus on the sustainability of national education.