Aplikasi Analisis Rantai Markov Untuk Memprediksi Status Pasien Rumah Sakit Daerah Kalabahi - Jeni Marianti Loban; Simei B. Libing (Peneliti.net, 2021)
Random events are events that occur randomly and cannot be predicted with certainty, therefore an appropriate model is needed to solve the problem of random events. A stochastic process is an approach that can be used to model an event that changes randomly in time parameters. Markov chain analysis is a discrete time stochastic process that must meet the Markov propert, namely the event at time t+1 depends on the event at time t. Markov chain analysis used in this study aims to predict patient status in the Kalabahi regional hospital. The data used is secondary data, namely data about the patient's condition when entering the Kalabahi Regional Hospital and the condition when leaving the Kalabahi Regional Hospital. The data obtained were 190 patients who were treated at the Kalabahi regional hospital in January 2021. The analysis began by classifying the condition or status of the patient into three, namely the condition (state) mild illness, condition (state) seriously ill and condition (state) referred. Next, a transition probability matrix (P) is made. This matrix is ​​then used to determine the steady state using the Chapman Kolmogorov equation. The results obtained are 99,0937% of patients who seek treatment at Kalabahi General Hospital will be discharged with a mild condition, 0% of patients will leave with a severe condition and 0,9063 will be discharged with a referral condition.