Application of Markov Chains in Predicting the Unemployment Rate in Pematangsiantar for the Years 2026–2030 - Nilam Sari; Gayus Simarmata; Yoel Octobe Purba (PT FORMOSA CENDEKIA GLOBAL, 2026)
The Open Unemployment Rate (OUR) is one of the key indicators used to describe the labor market conditions in a region. Fluctuations in the OUR over time indicate that labor market dynamics are uncertain and influenced by various economic factors. This study aims to apply the Markov Chain method to analyze and predict trends in the Open Unemployment Rate (OUR) of Pematangsiantar City for the period 2026–2030 based on historical data from 2017–2025. The method used in this study is a discrete-time Markov chain with two states: the “Decrease” state and the “Increase” state. The analysis was conducted by constructing a transition probability matrix, predicting probabilities, determining the steady state, performing retention time analysis and sensitivity analysis, and validating the model using backtesting and the R software package. The results show that the transition probability from the “Decreasing” state to the “Decreasing” state is 0.8, while the probability of transitioning from the “Increasing” state to the “Decreasing” state is 1.00. The prediction results show that the probability of a “Decline” condition during the 2026–2030 period ranges from 80% to 83.328%. Additionally, the steady-state results indicate a probability of 83.3% for a decline in the unemployment rate and 16.7% for an increase. The sensitivity analysis results show that changes in transition probabilities affect the steady-state results; however, the “Decreasing” condition remains the dominant state. Thus, the Markov chain method can be used to describe the probabilistic trends in the open unemployment rate in Pematangsiantar based on historical data transition patterns.