Data-Driven Mission Recommendation for LEO Satellites using Random Forest and Hybrid Deep Learning Forecasting
This paper presents a data-driven framework for LAPAN-IPB LEO satellites that combines a Hybrid SARIMAX-LSTM voltage predictor and a Random Forest classifier to generate auditable, evidence-based mission recommendations, thereby replacing reliance on undocumented operator experience with a reproducible, objective scheduling process.