Comparative Analysis of BMKG vs Accuweather Climate Forecasting Probability using Markov Chain

Abstract

Indonesia is a tropical country with a monsoon climate characterized by two main seasons: the rainy season and the dry season. These seasonal climate changes not only affect rainfall and air temperature but also significantly impact various aspects of people's lives. Annually varying climate conditions often create uncertainty in human activities, particularly in the agricultural sector, which relies heavily on water availability, the transportation sector, which is vulnerable to disruptions due to extreme weather, and the disaster management sector, which requires climate data-based planning. Therefore, an accurate, systematic, and reliable climate forecasting method is needed to support more informed decision-making. This study uses the Markov Chain method to model the probability of climate forecasting using data obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG) and Accuweather, utilizing the transition probability from previous to future climate conditions. Based on the results of the study, a comparison of the two data sources shows that BMKG is more dominant in predicting light rain with an average probability of 48%, while Accuweather tends to be more dominant in predicting cloudy conditions with a probability of 40%. With this model, daily climate predictions can be used to support decision-making and activity planning in Indramayu Regency, especially Balongan District.