Pengembangan Dashboard Prediksi Penggunaan Transportasi Umum Berbasis Business Intelligence dan Random Forest di Jakarta - Ahmad, Alifio Fikra Ahmad; Budy Santoso, Cahyono (Jurnal Algoritma, 2025)
This study applies the concept of Business Intelligence (BI) to predict and visualize trends in public transportation usage in Jakarta to support data-driven decision making. Secondary data from the Satu Data Jakarta portal was analyzed using the Random Forest algorithm due to its ability to process complex variables with accurate prediction results (R² = 0.978). The results show that TransJakarta, MRT, and KRL have stable passenger trends, while LRT, KCI Commuter Bandara, ships, and school buses are more volatile. These results are visualized in a web-based dashboard that facilitates fleet planning and public transportation operational policies. This research contributes to the application of BI in the transportation sector by presenting a prediction model that supports data-driven policy formulation.