APPLICATION OF MARKOV CHAIN IN MONTHLY RAINFALL PREDICTION IN AMBON CITY - Sahril G. Rumeon; Nurul Aulya; Silvia W. Telussa; Christi A. Patty; Fera F. Sopaliu; Fadila Rumalean; Chelsy T. Rumangun; Winda Tuankotta; Yudistira (LPPM Universitas Negeri Jakarta, 2025)
Asia has a tropical climate with two main seasons influenced by monsoons, namely the rainy season and the dry season. However, in recent years, seasonal patterns have shifted due to climate change, making it difficult to predict weather, including rainfall. Ambon City, as one of the regions with high and varied rainfall in eastern Indonesia, is highly dependent on weather conditions, especially since most of its inhabitants work as fishermen and farmers. Therefore, rainfall prediction is important to support appropriate decision-making in the marine, agriculture, and hydrometeorological disaster risk mitigation sectors. This study aims to model and predict the status of monthly rainfall in Ambon City in 2025 using the Markov chain method, a first-order probability-based approach that describes transitions between circumstances based on historical data, where the chances of subsequent events depend only on current circumstances. The data used is in the form of monthly rainfall from 2015 to 2024 obtained from the Pattimura–Ambon Meteorological Station. The data were classified into four categories of precipitation: light, medium, high, and very high, which were further used to compile a one-step probability transition matrix. The results showed that the steady-state distribution of rainfall in Ambon City tended to be in the moderate category (47.90%), followed by very high (26.5%), light (20.17%), and high (5.88%). The rainfall prediction for 2025 shows a transition pattern that is close to a steady state, where month after month there is a stable trend. With this information, fishermen can be wiser in determining safe times to go to sea, and the government can design climate change adaptation and mitigation policies more effectively.