This research aims to evaluate cabbage production planning in North Sumatra using the discrete-time Markov chain method. Cabbage is one of the horticultural agricultural products that plays an important role in North Sumatra's exports. Proper evaluation of production plans is necessary to ensure sustainability and increase productivity and export volume. The Discrete Time Markov Chain method is used to predict changes in cabbage production conditions over time by considering the factors that influence them. Data on cabbage production and harvested area in North Sumatra from 2020 to 2022 were analyzed using one-step and n-step transition opportunity matrices. The results of the analysis show that in 2023, cabbage production and harvested land area are predicted to experience a significant increase compared to the previous year. This research provides a more accurate and efficient planning strategy for cabbage production, which can ultimately improve agricultural management in North Sumatra.
 

This research utilizes the Markov Chain method to analyze daily weather data in the city of Medan. The main objective of this study is to forecast weather changes in the future based on the weather conditions of the previous day. Daily weather data was collected from the nearest weather station over a specific period of time. The analysis results indicate that the Markov Chain model provides good estimates of the likelihood of weather changes from one day to the next. The steady state probabilities demonstrate the dominance of partly cloudy and clear weather in the long term. This research provides valuable insights for various sectors related to weather, such as agriculture, transportation, and tourism.