Penerapan Algoritma Random Forest untuk Memprediksi Curah Hujan pada Masa Mendatang di Daerah Berpotensi Banjir

Palembang, as one of the largest cities in Indonesia, regularly experiences severe flooding problems every year. Flooding not only disrupts the daily activities of residents, but also causes significant economic losses and social impacts. To solve this problem, it is crucial to have an in-depth understanding of flooding patterns and some of the factors that influence them. The purpose of this research is to apply highly efficient Machine Learning (ML) technology for the prediction analysis of future flood-prone areas. The integration of ML can help in identifying patterns, predicting risks, and making more accurate decisions in flood mitigation. In an effort to achieve this goal, the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology will be applied to ensure the research is conducted systematically and comprehensively. Therefore, research on the analysis of mapping flood-prone areas in Palembang using ML is essential to provide a fairly effective and efficient solution to the long-standing flooding problem. With the CRISP-DM approach, it is expected that this research can produce an accurate and reliable prediction model by integrating the Random Forest algorithm as a regression model, and provide long-term benefits for flood risk management in Palembang and several other cities in Indonesia that experience similar problems.