PREDICTIVE ANALYSIS OF TOURIST ARRIVALS IN NORTH SUMATRA USING THE RANDOM FOREST ALGORITHM - Br. Pasaribu, Agustina Kristin; Arie Rafika Dewi (Bahasa Indonesia, 2025)
This study aims to develop an accurate predictive model for forecasting the number of international tourist arrivals to North Sumatra Province using the Random Forest algorithm. The data used is sourced from Open Data Sumut and includes historical data on tourist arrivals in previous years. Using the Random Forest Regressor, this model demonstrates high accuracy, with an R-Squared (R²) value of 56,06% and a low Mean Squared Error (MSE) of 36.087.182,23. The results of this study show that the year feature is more dominant in predicting tourist numbers compared to the month feature, indicating that annual trends have a greater influence on tourist arrival patterns than seasonal factors. The predicted number of tourists for July 2025 is 17.597,73