Analisis Metode Rantai Markov Untuk Memprediksi Status Pasien Di Pusat Kesehatan Masyarakat (Puskesmas) Guluk-Guluk Kabupaten Sumenep - Ulfatul Husna; Fathorrozi Ariyanto; Prasanti Mia Purnama (Alpha-Epsilon: Journal of Mathematics, 2025)
Markov chains are a method for predicting future events using certain techniques from current data. One application of the Markov Chain method is related to event prediction, such as predicting the number of patient statuses in a health center. It is hoped that predicting the number of patient statuses can be used in various program planning. This research using the Markov Chain method uses patient data in May 2023 at the Guluk-Guluk Health Center, Sumenep Regency. The data is analyzed to determine the transition from one state to another, then converted into a transition probability matrix and then searched for the steady state (equilibrium). Apart from being searched using the transition probability matrix, the steady state can be found using the substitution method. If the result values are aqual then the two method processes carried out are correct. The results of this research state that the Guluk-Guluk Community Health Center will experience steady state conditions that will occur in December 2024, with equilibrium conditions occurring in the 20th period. The respective transition probability values are 94.2% for improving conditions, 4 .9% for normal conditions, and 0.9% for severe conditions.